Recent Advances in Iterative Learning and Repetitive Control

Track proposed by:
Bing Chu, Tom Oomen, Kira Barton, Ying Tan, Pavel Pakshin

Abstract:
Iterative Learning control (ILC) and repetitive control (RC) are high performance tracking control design methods for systems operating in a periodic or repetitive manner. They adapt the control effort based on information collected from previous trials (periods) and thus can lead to significantly better performance than conventional control design approaches even without using accurate system model information. This open invited track aims to bring together results representing the dominant analysis and design paradigms, address new theoretical challenges, and present emerging and non-traditional applications.

Web site: none
Code for submitting contributions: 8c8sv
Full description: PDF

Identification, learning and control of quantum systems

Track proposed by:
Daoyi Dong, Jr-Shin Li, Bo Qi, Ian R Petersen

Abstract:
The emerging quantum technology has been widely recognized as one of promising future technologies. The theory and methods for identification, learning and control of quantum systems are fundamentally important for enabling the industrialization of quantum technologies including quantum computation, communication and metrology. This open invited track provides a forum for idea exchange in the active research area of identification, learning and control of
quantum systems, as well as their applications to practical quantum technologies.

Web site: none
Code for submitting contributions: n72ag
Full description: PDF

Event-Triggered and Self-Triggered Control

Track proposed by:
Maurice Heemels, Sandra Hirche, Karl H. Johansson, Michael Malisoff, Cameron Nowzari, Romain Postoyan

Abstract:
This Open Invited Track is seeking contributions in the broad areas of Event-Triggered and Self-Triggered Control (ETC and STC). In addition to papers proposing new ETC and STC strategies and/or providing new theoretical results, we also explicitly solicit papers that show the potential of ETC and STC in experimental setups and real-life applications.

Web site: none
Code for submitting contributions: w8u4j
Full description: PDF

Tensor Methods for Modelling and Control

Track proposed by:
Gerwald Lichtenberg, Kim Batselier

Abstract:
Tensors are a basic modelling structure for multidimensional problems in physics for more than a century.  
In the last decade, new results on tensor based algorithms have been achieved in modelling and control. This open invited track welcomes all application and theory related submissions showing the use of tensors and tensor decompositions for modelling, system identification, data analysis, (supervisory) controller design, fault diagnosis and reconfiguration in control engineering.

Web site: none
Code for submitting contributions: i8axq
Full description: PDF

Intelligent data-driven fault diagnosis, prognostics and health aware control

Track proposed by:
Mirko Mazzoleni, Silvio Simani, Mayank Shekhar JHA, Jacques Noom, Horst Schulte, Michel Verhaegen, Didier Theilliol

Abstract:
The complexity of modern industrial plants, along with the major investment in process data collections, have increased the attention towards data-driven methods for diagnosis and prognostic, as a fundamental tool to implement predictive maintenance schemes. Along the same line, safety-critical plants need robustness against process faults and deteriorations. The recent concept of health aware control is rapidly emerging as a way to include system health information into the control actions. This open invited track aims to face current challenges in data-driven diagnosis, prognostics and health aware control, discussing methodologies and applications. 

Web site: none
Code for submitting contributions: 58mj9
Full description: PDF

Control and estimation of dynamic systems on time scale: Methods and Applications

Track proposed by:
Taous-Meriem Laleg, Bacem Ben Nasser, Mohamed DJEMAI

Abstract:
Continuous and discrete mathematical models are commonly used to describe physical systems. However, various systems exhibit hybrid structures where both continuous and discrete events co-exist. The presence of discrete events can be attributed, for instance, to existing switches in the systems or in some interruptions.  The current continuous or discrete theory does not consider the two dynamics at the same time. Therefore, a more general theory that unifies the continuous and discrete models provides an opportunity to better describe and control dynamic systems. Time scale dynamic representation provides a unified framework for systems with both continuous and discrete dynamics. They allow the consideration of systems evolving on various time domains including uniform and nonuniform domains.  
The mathematical analysis of dynamical systems is an active research area where the standard control concepts have been extended to dynamic systems evolving on arbitrary time scales.  There are still many open questions in the field.  The objective of this open invited track is to present the recent advances and to discuss the current challenges in analysis, control design, and observer design of dynamical systems on a time scale. Both theoretical and application-oriented contributions are welcome.

Web site: none
Code for submitting contributions: thqm5
Full description: PDF

Data-driven modeling and learning in dynamic networks

Track proposed by:
Paul M.J. Van den Hof, Donatello Materassi, Shengling Shi

Abstract:
With the growing spatial complexity of engineering systems, e.g., in power networks, transportation networks and industrial production systems, also referred to as cyber-physical systems of systems, there is a strong need for effective modelling tools for dynamic networks, being considered as interconnected dynamic systems, whose spatial topology may change over time. A similar development can be observed in (bio)physical systems as e.g. in systems biology and neurosciences. Consequently there is a strong need for developing methods for data-driven modelling, learning and diagnostics in dynamic networks.  
Both identification methods and machine learning methods are currently being developed to provide data-driven tools for modeling and learning in systems that are structurally interconnected in dynamic networks. The modeling and learning aspects include estimation of dynamics as well as interconnection structure (topology) and cause-effect relationships. Moreover the selection of relevant signals, and the setup of appropriate experiments (excitations), will be considered, as well as aspects of distributed computational resources, as e.g. in distributed estimation, modeling, diagnostics  and control.    
This invited track intends to bring together researchers in academia and industries working on the emerging topic of data-driven modeling and learning in dynamic networks.

Web site: none
Code for submitting contributions: 9v3i4
Full description: PDF

Modeling and Design of Secure and Resilient Control Systems

Track proposed by:
Michelle Chong, Qing-Long Han, Craig G. Rieger, Henrik Sandberg, Quanyan Zhu, Yuhan Zhao

Abstract:
The security of control systems is challenging, not only due to the cross-layer and system-wide needs but also because of the role of the attacks. Growingly sophisticated attacks make it harder or infeasible to achieve “perfect” security, which aims to protect control systems from attacks and ensure system performance. Resilience becomes an effective way to further mitigate security risk. This track aims to create a platform between practitioners and control theorists to share knowledge and insights on the modeling and design of next-generation high-confidence control systems that are secure and resilient to a growing number of cyber threats.

Web site: none
Code for submitting contributions: 4bi4c
Full description: PDF

Fractional order differentiation in modeling and control

Track proposed by:
Stephane Victor, Pierre MELCHIOR 

Abstract:
Fractional (or non-integer) differentiation has played a very important role in various fields notably in signal and image processing and control theory. In these last fields, important considerations such as modeling, system identification, stability, controllability, observability and robustness are now linked to long-range dependence phenomena. It is expected that such an open invited track will attract new researchers regarding the growing research and developments on fractional calculus in the areas of mathematics, physics, engineering and particularly in automatic control.
This invited session is devoted to research topics in the field of fractional calculus in order to present and to discuss the latest results in fractional dynamical systems and signals domain:
- Signal analysis and filtering with fractional tools (restoration, reconstruction, analysis of fractal noises)
- Fractional modeling especially of (but not limited to) thermal systems, electrical systems (motors, transformers, skin effect, etc.), dielectric materials, electrochemical systems (batteries, ultracapacitors, fuel cells, etc.), mechanical systems (vibration insulation, viscoelastic materials, etc.), biological systems (muscles, lungs, etc.)
- Fractional system identification (linear, nonlinear, multivariable methods, etc.)
- Implementation aspects (fractional controllers and filters implementation, etc.)
- Systems analysis (stability, observability, controllability, etc.)
- Control (Fractional PID, CRONE, H∞, etc.)
- Diagnosis of fractional systems
- Fractal structures, porous materials, etc.
- Applications (mechatronics, automotive, medical/biological systems,…)

Web site: none
Code for submitting contributions: w7896
Full description: PDF

Set-Valued Approaches to Control and Estimation of Uncertain Systems

Track proposed by:
Thach Ngoc Dinh, Andreas Rauh, Sze Zheng Yong, Zhenhua Wang

Abstract:
We provide an open invited track for presenting and discussing the latest developments on theoretical as well as computational aspects of set-valued and interval methods in control and estimation of uncertain systems across different academic disciplines (electrical engineering, mechanical engineering, chemical  engineering) and from different geographic regions. While all the papers center around the theme of the issue, they cover a wide range of theoretical and application topics of great interest to control researchers from academia and industry.

Web site: none
Code for submitting contributions: td76r
Full description: PDF

Adaptive and Learning Control for Robotics and Dynamical Systems

Track proposed by:
Prof. Ahmad Taher Azar

Abstract:
In the recent decade, adaptive control for robotics and dynamical systems has been developed, and learning control design is still in its early stages. Control system design is an important phase in the development of complex dynamical systems and robotics. Research on robotics and automation has made significant progress in both theoretical investigation and practical applications. Advances in sensors, actuators, computation technology, and communication networks help provide the necessary tools for the implementation of hybrid control hardware. This open invited track aims intends to bring together scholars to present the most recent advances and innovations in the subject of adaptive and learning control for robotics and dynamical systems with the goal of summarizing and improving the techniques in this field. The topics covered here are closely related to the TC 2.1 research areas and we look forward to receiving many contributions to this track from the TC 2.1 and many others.

Web site: none
Code for submitting contributions: bnr12
Full description: PDF

What can Control Theory do for Robust and Safe Learning?

Track proposed by:
Mario Sznaier, Tiago Roux Oliveira, Milad Siami, Eduardo Sontag

Abstract:
The combination of the exponential explosion in sensing capabilities and unprecedented advances in Machine Learning (ML) have opened up the possibility of having truly autonomous systems capable of safely learning from and interacting with the environment. Indeed, recent results on reinforcement learning offer a tantalizing view of the potential of a rapprochement between control and learning. However, most of the research at the confluence of control and ML seeks to address the problem of ``what can ML do for Control?"" and concentrates on using Machine Learning techniques to synthesize controllers from data, often avoiding  Systems Identification and  model-based control design steps. This open track focuses on the less explored dual problem of ``what can  Systems Theory do for Learning?"" We believe that this less explored area can be a rich source of problems  at the confluence of Machine Learning and Control that do not necessarily involve learning a controller. Examples range from   the use of control-theoretic tools to analyze and certify the learning properties of Neural Networks to the exploration  of several aspects unique to learning dynamical systems that make these problems considerably harder than more traditional Machine Learning problems, including sample complexity, generalization properties and certifying safety while learning.

Web site: none
Code for submitting contributions: 8258t
Full description: PDF

Recent Advances in Automated Learning and Calibration of MPC Policies

Track proposed by:
Dinesh Krishnamoorthy, Ali Mesbah

Abstract:
There is a rapidly growing interest in using learning and data-driven techniques for the development of different Model Predictive Control (MPC) formulations. The use of learning tools has gone beyond the more traditional approach of adapting/learning the prediction model or uncertainty description, to more generalized parametrized control policies.  Currently there is a lot of research activity in this domain with different approaches to learn the parameters of the policy that would result in a desired performance. For example, the policies can either be learned from expert demonstrations in the context of imitation learning, or using closed-loop performance data in the context of “tuning” or reinforcement learning. We expect that this Open Invited Track will serve as a forum to encourage and discuss new developments at the boundary between model predictive control, reinforcement learning and data-driven optimization. The topics of this Open Invited Track include but are not limited to: MPC tuning using Bayesian optimization; reinforcement learning and MPC; Approximation of complex MPC laws using machine learning; and online calibration of learned policies. We welcome novel theoretical contributions, practical implementations and tools, as well as challenging case studies.

Web site: none
Code for submitting contributions: 82e65
Full description: PDF

Recent trends in modeling, simulation and control of Distributed Parameter Systems

Track proposed by:
Yann Le Gorrec, Hector Ramirez

Abstract:
Nowaday, partial differential equations (PDEs) play a central role in the modeling of dynamical phenomena involved in cutting edge engineering applications. Although the study of PDE systems is quite well established within the mathematical community, the analysis and control of distributed parameter systems governed by PDEs remain a challenging topic in regard to the complexity of the considered applications. With the proposed open invited track entitled ""Recent trends in modeling, simulation and control of Distributed Parameter Systems"" we aim at stimulating and fostering exchanges and discussions of both mathematical and engineering communities working on modeling, simulation, estimation, and control of systems governed by PDEs. The aim is also to provide a state-of-the-art forum on recent progresses recently established in this field of research with both academic and industrial communities.

Web site: none
Code for submitting contributions: 2s8sb
Full description: PDF

Asymptotic and Non-Asymptotic Estimation Methods for Nonlinear Systems

Track proposed by:
Ibrahima N'Doye, Taous-Meriem Laleg

Abstract:
This session aims to present recent advances in the design of asymptotic and non-asymptotic estimation algorithms and state observers for nonlinear systems and their applications. The development of fundamental and theoretical estimation methods with different techniques and real-world applications is considered.

Web site: none
Code for submitting contributions: hbx25
Full description: PDF

Estimation and Observer Design: Theory and Applications

Track proposed by:
Ali Zemouche, Zehor Belkhatir, Ankush Chakrabarty, Rajesh Rajamani

Abstract:
The objective of this open invited track proposal consists in inviting contributions in the field of linear and nonlinear estimation and their applications in many theoretical and practical problems. The main goal is to bring together experts in the field of estimation and application-oriented researchers to create fruitful discussions on the recent advances and identify some future directions.

Web site: none
Code for submitting contributions: 7f2x9
Full description: PDF

Data-driven Methods in Control: Analysis, Feedback Design and Optimization

Track proposed by:
Julian Berberich, Florian Dorfler, Timm Faulwasser*, Paolo Rapisarda, Henk J. van Waarde, Karl Worthmann

Abstract:
Data-driven methods have been used for system analysis, identification, and control for decades. Likewise, data-driven techniques for feedback design are common in adaptive control and they have become increasingly popular in model predictive control. The unifying idea is to use data to fit, improve or even completely substitute a model, e.g., to predict the dynamical system behavior. This enables data-driven and optimization-based controller design, analysis, and synthesis.   
With the ever increasing availability of both data and computing resources and in parallel to the recent trend toward the inclusion of machine learning, data-driven approaches are attracting interest from research and applications in systems and control. Nevertheless, key challenges remain in the process of data-based control design: certified controller performance, incorporation of prior knowledge, self-tuning during runtime to name just a few.   
In this open invited track, we bring together experts from various branches of systems and control to assess current trends of data-driven analysis and control design from different, complementing viewpoints. Thereby, we aim to understand the theoretical foundations based on the current state of research and in view of future applications.  
The track welcomes contributions on theoretical and methodological aspects of data-driven system analysis, control, and optimization.

Web site: none
Code for submitting contributions: qdnxh
Full description: PDF

Dynamics, Stability, and Control of Systems with Time Delays; Theory, Scientific Computation, and Applications

Track proposed by:
Islam Boussaada, Wim Michiels, Tamas G. Molnar, Gabor Orosz, Rifat Sipahi, Tomas Vyhlidal

Abstract:
This open invited track is proposed on the topic of dynamics, stability, and control of systems affected by time delays, and it is open to contributions including new results in and combinations of theory, scientific computation, and applications.  
This track is proposed by the above listed members of the Technical Committee TC 2.2 on Linear Control Systems. Many members of this technical committee for over two decades have been leading efforts in organizing the IFAC Workshop on Time Delay Systems. More precisely, this series started in1998 and in 2022 the workshop will be held in person in Montreal, Canada with around a hundred registered participants. There is also a very active working group on time delay systems whose quarterly webinars are very well attended (typically 100-200 participants). With this open invited track, the goal is to continue the tradition of bringing the scientific community together in the area of systems with time delays. We hope that this invited open track will enable a platform to discuss current research developments and open problems as well as to establish scientific networks.   
Time delays appear in a multitude of applications and are often attributed to the root cause of poor performance or instability, if not dealt with properly. An interest in studying dynamical systems and how to control them at the face of time delays has therefore been the attention of engineers, mathematicians, and physicists. Application domains of systems with delays can even be considered larger, expanding into biology, social sciences, economics and artificial intelligence. With many technological advances in network control systems, connected dynamical systems and with the emergence of low cost sensor/actuation/computer systems, the importance of systems with delays is only destined to further grow, as we envision a future with connected, intelligent, autonomous vehicles, networked robotic systems, human-robot interactions, cyber physical systems, and distributed sensor systems. 

Web site: none
Code for submitting contributions: 3gi38
Full description: PDF

Model reduction for modeling, analysis and control – Methods and applications

Track proposed by:
Martin Monnigmann, Thomas Meurer, Alessandro Chiuso

Abstract:
Model reduction methods are established tools in the field of automatic control. Conversely, automatic control has traditionally been a driver for the advancement of model reduction methods. We invite both, contributions that require model reduction as an enabling step for automatic control, and contributions on the advancement of model reduction methods for use in systems and control. Contributions that involve learning or other data-driven approaches, or compare data-driven approaches to model reduction methods that require physical models are particularly welcome.

Web site: none
Code for submitting contributions: mip4p
Full description: PDF

Thermodynamics Foundations of Mathematical Systems Theory

Track proposed by:
Nicolas Hudon, Ngoc Ha Hoang, Gabor Szederkenyi, Denis Dochain

Abstract:
Contributions on modeling, systems analysis, and control design to complex physical systems are central to the development of automatic control.  As a result, techniques for analysis and feedback control design for physical representations based on thermodynamics have emerged in recent years and proved useful in theory and applications alike.    In particular, extensions of structure-modeled systems, for example port-Hamiltonian systems, originally developed for electro-mechanical systems, to thermodynamic systems generated novel approaches within the field of automatic control.  Moreover, the development of structured-preserving numerical methods for thermodynamic systems shed a light on the the potential interactions between the fields of numerical analysis and feedback control systems analysis for distributed parameters systems.  The relation between systems theory, as understood by researchers and practitioners in automatic control, and thermodynamics is an active scientific area.  Classical extensions of dissipative systems theory to dynamical systems with inputs and outputs under thermodynamic constraints led, in recent years, to numerous results ranging from investigation son the proper geometric framework for feedback control design to results on stability analysis, both for deterministic and stochastic systems where thermodynamics play a prominent role.  Applications where a physically-consistent control theory for thermodynamic systems is needed include sustainable energy production, chemical reaction networks analysis, and quantum systems.  The objective of the proposed Open Invited Track is to gather contributions from systems and control practitioners and researchers interested in thermodynamics theory and its extensions in the context of control systems.  Contributions are expected to include modeling, analytic and geometric methods, as well as feedback control design methodologies for systems where thermodynamics theory is the fundamental science.

Web site: https://tfmst2022.engineering.queensu.ca/home/
Code for submitting contributions: 51p6d
Full description: PDF

Port-Hamiltonian systems in modeling, simulation and control

Track proposed by:
Hector Ramirez, Yann Le Gorrec, Bernhard Maschke

Abstract:
It has been 30 years since the seminar paper """"Port-Controlled Hamiltonian Systems: Modelling Origins and Systemtheoretic Properties"""" by B. Maschke and A. van der Schaft was presented in NOLCOS'92. Since then the framework, applications and related research community have steadily grown and expanded to encompass the modeling, control and simulation of non-linear systems, distributed parameter systems, irreversible thermodynamic systems, networked controlled systems, optimal control, spatial and time discretization, quantum systems, just to mention a few. Although Port-Hamiltonian systems are nowadays a well established formalism within the control community there are several open and challenging topics in regard to the complexity of the considered applications.   
This open invited track aim to provide a well-balanced combination of recent theoretical results and their applications for mathematical modeling, structure-preserving approximation, model-based analysis, and observer and control design for lumped and distributed parameter systems using the framework of port-Hamiltonian systems. The track will bring together researchers from mathematics, engineering and the industrial community working in related fields or facing related control issues. The track will provide a forum for presenting and discussing the latest developments on theoretical, applied and computational aspects of the control of port-Hamiltonian systems by leading researchers. The organizers aim to include contributions from both junior and senior faculty across different academic disciplines, i.e., electrical engineering, mechanical engineering, chemical engineering, applied mathematics, and representing different geographic regions. While all contributions will center around the track theme, they should cover a wide range of theoretical topics and applications that are of great interest to control researchers from academia and industry. 

Web site: none
Code for submitting contributions: ps3q8
Full description: PDF

RECENT ACHIEVEMENT AND PERSPECTIVE DIRECTIONS IN SMC

Track proposed by:
Leonid Fridman

Abstract:
September 18th, 2022 Professor Utkin one of the originators of Variable Structure and Sliding Mode Theory died. That is why now it is a correct moment to organize the open track memorizing the originators of Variable Structure and Sliding Mode Theory and to revisit the recent results in sliding mode control and to draw the next most perspective lines of development. The following  main lines of investigation are planned to be be reflected:
(i) homogeneous and bi -homogeneous sliding mode algorithms(continuous and discontinuous);
(ii) sliding mode based observation, identification, fault detection and fault tolerant control;
(iii) comparison of conventional and high-order sliding mode controllers, chattering analysis in both frequency domain and state-space ;
(iv) discrete sliding modes;
(v) adaptation of sliding mode control algoritms;
(vi) application  of the sliding mode control: control of under actuated systems, aerospace applications, networked control, event triggered control, and others.

Web site: none
Code for submitting contributions: 7854c
Full description: PDF

Recent Advances in Fuzzy Model-Based Control

Track proposed by:
Jun Yoneyama, Zsofia Lendek, Tadanari Taniguchi, Kevin Guelton

Abstract:
The aim of this open invited track is to present state-of-the-art results in the area of theory and applications of fuzzy-model-based control design and analysis at large, and to get together well-known and potential researchers in this area, from both the academia and industries. Fuzzy-model-based control provides a systematic and efficient approach to the analysis and control of nonlinear systems. It has been employed to deal with a wide range of nonlinear control systems such as continuous-time, discrete-time, hybrid, sampled-data, time-delay, switching, adaptive control systems and so on. However, there is still room for improvement of the existing results in order to propose new techniques for control. This open invited track focuses mainly on the fuzzy-model-based control systems and analysis with emphasis both on theory and applications. Important problems and difficulties in fuzzy-model-based control systems will be presented, solutions will be provided and methodologies will be proposed to handle nonlinear systems using fuzzy-model-based control approaches. The session will cover classical Takagi-Sugeno fuzzy model, Type 2 and polynomial fuzzy models for stability, control and estimation, representing an important field of the TC 3.2, Computational Intelligence in Control.

Web site: none
Code for submitting contributions: nsk55
Full description: PDF

Transformation of legacy software in manufacturing and logistics systems as enabler for reconfigurable, agent-based automation architectures

Track proposed by:
Birgit Vogel-Heuser, Hartwig Baumgärtel, Mike Barth, Elisabet Estévez, Alexander Fay, Alois Zoitl

Abstract:
Automated production systems, including manufacturing and intralogistics systems, are facing increasing challenges to adapt to the recent technological leaps in the context of Industry 4.0 and to compete on the global market by ensuring evolvability to changed requirements during lifecycles of several decades. Agent-based automation architectures have proven as suitable means to enable adaptable production systems and to automatically reconfigure such systems in case of faulty or broken physical components enabling continuing operation instead of downtime.  One key challenge to the applicability of such agents is the creation of individual knowledge bases, e.g., based on engineering models, common ontologies or by learning from operational data during operation of manufacturing and logistics. Additionally, the implementation of multi-agent systems in manufacturing requires modular, evolvable automation software. In reality, however, automation software does often not evolve systematically but is enlarged by extensions in the code to implement new requirements as quickly as possible, leading to historically grown legacy software that is difficult to maintain and reuse. For a successful integration of agent-based approaches in industry, it is essential to migrate the domain knowledge of controlled processes from existing legacy software rather than restarting from scratch. Thus, this Open Invited Track aims to present innovative approaches on the analysis, refactoring, and quality assurance of modular automation software as well as on reconfigurable, agent-based automation architectures.

Web site: none
Code for submitting contributions: x829e
Full description: PDF

Multi-objective optimization techniques in control systems engineering

Track proposed by:
Gilberto Reynoso-Meza, Xavier Blasco

Abstract:
Control engineering problems are generally multi-objective; several specifications and requirements must be fulfilled, often in conflict. A traditional approach for calculating a solution with the desired trade-off is defining an optimization statement. Multi-objective optimization techniques deal with such a problem from a particular perspective by searching for a set of potentially preferable solutions: the so-called Pareto set. The designer may then analyze the trade-off among solutions in this set and select a preferred alternative according to the problem. This open track aims to provide practitioners the opportunity to exchange ideas and share potential applications of multi-objective optimization techniques in control systems engineering. This track follows its previous editions in 2017 and 2020 and focuses on using or extracting information from a Pareto front approximation to solve a control problem. Topics covered (but not limited to) include insights, tools, and theoretical developments on Multi-objective problem definition; Multi-objective optimization process; Multi-criteria decision-making stage; Modelling for control; Controller design, and tuning.

Web site: none
Code for submitting contributions: 451eu
Full description: PDF

Advances in Machine Learning and Intelligent Control for Industrial Automation and Robotics

Track proposed by:
Seán Francis McLoone, Lucian Busoniu, Gian Antonio Susto

Abstract:
The constantly increasing availability of data, the rapid expansion in computational and storage capacities of IT systems, and algorithmic advances in Machine Learning, Artificial Intelligence and Intelligent Control, are beginning to have a huge impact in many areas of science and engineering. Breakthroughs in Deep Learning, in particular, have led to major advances in Computer Vision and Natural Language Processing over the last decade. Coupled with the developments in the Internet of Things (IoT), these technologies are transforming many sectors of our society, and are at the heart of the envisaged transformation of industry -- the so-called fourth industrial revolution, a.k.a. Industry 4.0.   
The objective of this Open Track is to showcase real-world applications and algorithmic advancements of Machine Learning and Intelligent Control that are designed to solve the problems faced by data scientists and researchers when moving from proof of concept studies to productive Industry 4.0, robotics and automation solutions.   
We are particularly interested in contributions from industrial practitioners who may find the option of 2-4 page discussion papers an attractive submission format. We invite contributions that are based on (but not limited to) the following methodological sectors:  
- Parsimonious and robust Machine Learning approaches;  
- Deep Learning;  
- Machine Learning and Control;  
- Machine Learning approaches for sequence learning tasks;  
- Reinforcement Learning;  - Computer Vision;  
- Natural Language Processing;  
- Learning and system identification.  
Application areas include flexible automation and robotic systems, human robot collaboration, predictive maintenance, anomaly detection, soft sensing, and autonomous inspection systems.

Web site: none
Code for submitting contributions: wmhfs
Full description: PDF

Machine learning and big data applied to energy storage system modeling and control

Track proposed by:
Changfu Zou, Yicun Huang, Remus Teodorescu, Nadia Yousfi-Steiner, Weihan Li, Davide Martino Raimondo, Ramon Costa-Castelló, Zahra Nozarijouybari, Hosam K. Fathy

Abstract:
The rapid growth in the vehicle electrification and grid storage market has given rise to the need for intelligent and efficient modeling and control of energy storage systems. Despite the popularity of physics-based models in energy-related research, their applications have been greatly hindered by computational burden and parameterization complexity. Machine learning and big data, proven successful in many other disciplines, have sparked tremendous research interests in the field of energy storage. This open invited track provides an opportunity for like-minded researchers to explore, advertise and exchange works at a time frame where the trend of integrating machine learning and big data in control strategies has become eminent. The discussions that follow are expected to make an immediate impact on the participating researchers and initiate collaborations between research groups around the globe. 

Web site: none
Code for submitting contributions: 457a7
Full description: PDF

The Enterprise of the Future as a Complex Cognitive System

Track proposed by:
Ioan Dumitrache, Simona Iuliana Caramihai, Peter Kopacek, Florin Gheorghe Filip, Ioan Stefan Sacala, Mihnea Alexandru Moisescu

Abstract:
Cognitive Manufacturing Systems were addressed primarily in the context of Industrie 4.0 and considered as an application of the concept of cognitive computing/ cognitive technologies in manufacturing enterprises. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms, data mining, pattern recognition and natural language processing, the computer system should be able to mimic the way the human brain works. Businesses can use it to incorporate all kinds of risk factors into a decision before providing a company with a recommendation about an investment or a location to build a new satellite office. The possibilities for this technology in the future are enormous, and no industry or socio-economical area will be left untouched by it in the next decade. This is the reason for, the term of “enterprise of the future” is not necessarily linked with manufacturing, but can be extended in education, e-health, agriculture, transportation, smart communities – practically any system that suppose complex networking of autonomous and goal-oriented entities, whose emerging behavior has to adapt to a highly dynamical and loosely defined environment. Paradigms as Cyber-physical Systems and technologies as IoT and IoS represent important supportive concepts, but the focus of the Complex Cognitive Systems approach is towards knowledge-based systems, behavioral modeling, model-driven software engineering, verification & validation of emergent behavior, evolutionary algorithms – which should be both practically oriented (in terms of goals and data mining) and theoretically supported (in terms of models). Achievement of control/ management goals is mostly based on communication and cooperation between subsystems, using cognitive, eventually biologically inspired problem-solving techniques, based on adaptive sensing definition and focus. The track welcomes papers which focus on applying the cognitive approach on complex networked systems with qualitatively and quantitatively defined goals, acting in dynamic environments.

Web site: none
Code for submitting contributions: e13qn
Full description: PDF

Learning for multi-robot and networked systems

Track proposed by:
Bart De Schutter, Lucian Busoniu, Stefan Sosnowski

Abstract:
Multi-robot systems involve two or more autonomous robots that are working together to achieve one or more well-defined objectives. Individual robots may be rather simple and by themselves unable to achieve the desired goals. However, the real power – and at the same time also the major challenge – lies in the cooperation and coordination of the individual robots so as to jointly achieve the specified objectives. Multi-robot interaction is moreover subject to challenges stemming from the networked and communication structure of the system. Although multi-robot systems have attracted significant attention worldwide, research in this area is still in its infancy.  
This open invited track aims at bringing together contributions covering the broad area of computational-intelligence, machine-learning, networked-control and AI-based methods for multi-agent decision-making in multi-robot systems as well as for coordination in networked systems in general. In addition to papers proposing new fundamental results, we also explicitly solicit papers that show the potential of multi-robot interaction and coordination in networked systems in experimental set-ups and real-life applications. Authoritative survey papers are also welcome.

Web site: https://seaclear-project.eu/103-ifac-world-congress-2023-proposal
Code for submitting contributions: 5fic1
Full description: PDF

Smart strategies and Intelligent techniques applied in Engineering

Track proposed by:
Pedro Cabrera, Juan Manuel Escaño, Martin Bogdan, Eloy Irigoyen, Matilde Santos

Abstract:
From the use of several techniques, obtained from the Artificial Intelligence and Soft Computing fields, in combination with traditional Automatic Control and Mechanical methods have emerged several Intelligent Control (IC) techniques applied in Engineering. Many of them have demonstrated novel solutions to solve hard engineering-specific problems with a higher reliability level. This relatively new field involves the use of several techniques such as neural networks (static and dynamic), evolving algorithms (genetic algorithms, swarm optimization, ant colonies, etc.), fuzzy logic (Mamdani and Takagi-Sugeno), among others, with hybridization of all of them with automatic control techniques and mechanical frameworks or applications.   
Additionally, in the last years, several Smart strategies have been proposed to offer new solutions to large-scale problems such as the integration of renewable energies in energy systems, the management of energy in autonomous systems, etc.  
The objective of this Open Track is to present all possible engineering solutions and applications which implement or propose some intelligent technique or smart strategies to tackle the problem candidate to be solved.  
We are interested in a wide spectrum of contributions: from practical engineering applications which use some intelligent techniques combined with control algorithms and mechanical interfaces to show the reliability of some solutions; to the analysis of smart strategies proposals applied to concept case studies at a planning level. 

Web site: none
Code for submitting contributions: dhfj6
Full description: PDF

Advanced Control of Human-Robot Interaction in Extreme Environments: From Theory to Applications

Track proposed by:
Ziwei Wang, Bo Xiao, Allahyar Montazeri, H. K. Lam, Xiaojie Su, Hongyi Li, Hongjing Liang, Yanpei Huang

Abstract:
Interaction control can take opportunities offered by contact robots as they are physically interacting with their human users in applications, such as intelligent gravity support for ergonomic manufacturing and teleoperated "three hand surgery" using a soft endoscope equipped with tools. Over the last decades, we have witnessed the burgeoning field of interaction control in the control theory and machine learning communities, by analyzing the exchange of haptic information between the robot and the human user, and how they share the task effort. Estimation and learning methods predicting human user intention with a large level of uncertainty, variability, and noise, when limited observation of human motion is available, have played an important role in constructing the control framework. Based on this motion intent core, recent methods of haptic, communication, and game theory have considered the co-adaptation of human and robot control and yielded a versatile interactive control pattern.
This special session is designed to serve researchers and developers to publish original and innovative work as well as the state-of-the-art algorithms and architectures for contact robotic control problems with applications in motor control, virtual and augmented reality, neural networks, and intelligent interfaces. This special session provides a platform for academics, developers, and industry-related researchers belonging to the vast communities of "Robotics", "Control Theory", "Neural Networks", "Computational Intelligence", "Machine Learning", "Deep Reinforcement Learning", and "Motor Control", to discuss, share experience and explore new areas of the intuitive human-machine interaction to solve the problems for a range of applications.

Web site: none
Code for submitting contributions: j35t3
Full description: PDF

Benchmark Problem on Control System Design of Hard Disk Drive with a Dual-Stage Actuator

Track proposed by:
Mitsuo Hirata, Takenori Atsumi, Shota Yabui, Takeyori Hara, Atsushi Okuyama

Abstract:
This open invited track aims to present various control system design approaches for hard disk drives with dual-stage actuators. Authors are requested to evaluate the control performance of the proposed method using the simulation program in the hard disk benchmark problem. This enables us to make a fair comparison of the control performance of the proposed methods. Not only regular papers but also discussion papers are acceptable in this invited track.

Web site: none
Code for submitting contributions: m5miv
Full description: PDF

Mechatronics tools and control related to robotic manipulation

Track proposed by:
Mauze Benjamin, Mourad Benoussaad, Micky Rakotondrabe

Abstract:
Robotic manipulation consists in the use of mechatronic devices, for instance grasping tools together with robot arms, to perform manipulation or grasping of objects. Applications can be found in macro and in miniaturized scales, for instance: automotive industry, medical surgery and assistance, industry of future, or military. Many challenges are observed in robotic manipulation from the design point of view to the control one: limited number of sensors, difficulty to integrate appropriate actuators, modeling of complex system, architecture. On the other hand, we witness the last decade novel scientific stimuli: robotic manipulation in presence of or with human, tasks in harsh - extreme or tiny environment, manipulation tasks with small objects where surface forces are more important than the weight, more and more complex objects shape, deformability of the objects, more and more dexterity to be reached, multi-fingered manipulation, grasping stability, manipulation based on mobile robotics platform... Hence new advanced modeling, control techniques and related tools have also been raised for the mechatronic devices that equip these robots and for these latter as well.  
This open invited track is to create the opportunity of bringing together the researchers from the communities of mechatronics, robotics and control to propose innovative solutions and methodologies to succeed the tasks of manipulation. The expected papers are related to new design and development of mechatronic systems for and to control in robotic manipulation in general.

Web site: http://m.rakoton.net/ITifacWC23mechatronics.php
Code for submitting contributions: a8ku8
Full description: PDF

Smart materials based mechatronic systems and structures: control aspects

Track proposed by:
Micky Rakotondrabe

Abstract:
Smart materials are materials that exhibit inherent properties change when subjected to external stimuli. When the property change yields deformation, displacement or force and stress, one can develop actuators. This open invited track is interested in mechatronic systems and structures that use smart materials based actuators, including but not limited to: piezoelectric, shape memory alloy, magnetic shape memory alloy, electroactive polymers, magnetoactive polymers, magnetostrictive, magnetic fluid, or electrical fluid actuators. One of the main advantages from those mechatronic systems and structures is the high resolution of positioning – sometimes down to nanometers, allowing them to be used in applications that require high precision. In counterpart, they exhibit strong nonlinearities due to the complex behaviors of the smart materials actuators. Moreover, certain systems and structures can exhibit unwanted or complex dynamics such as badly damped vibration. Also, the functioning principles of certain mechatronic systems are non-standard so that their modeling and control are very specific: stick-slip, inch-worm, dual-stage, hybrid actuation, … Finally, in some of their applications, the lack of appropriate sensors is a major problem that prevents from feedback control. As a summary, smart materials based mechatronic systems and structures raise several challenges when it comes to control aspects.  
We propose in this open invited track the opportunity to bring together recent works on control aspects related to smart materials based mechatronic systems and structures. The expected papers include: modeling, signal estimation, identification and control.

Web site: http://m.rakoton.net/ITifacWC23SmartMechatronicsStrctures.php
Code for submitting contributions: 6v3j8
Full description: PDF

SYSTEM IDENTIFICATION for MANUFACTURING CONTROL APPLICATIONS

Track proposed by:
Natalia Bakhtadze, Kirill Chernyshov, Elena Jharko

Abstract:
The SIMCA Open Invited Track aims to bring together scientists working in all branches of control theory to discuss, in the light of manufacturing control problems, issues relating to development of the theory and methodology of identification, corresponding mathematical problems, parameter and non-parametric identification, structure identification and expert analysis, problems of selection and data analysis, control systems with an identifier, identification in intelligent systems, simulation procedures and software for identification and modeling, cognitive issues of identification, verification and problems of software quality for complex systems, global network resources of support processes of identification, modeling, and control.

Web site: none
Code for submitting contributions: wftqn
Full description: PDF
 

Information and Control Systems in the Cyber-Physical Enterprise

Track proposed by:
Herve Panetto, Georg Weichhart, Arturo Molina

Abstract:
The cyber-physical enterprise is a digital business innovation concept making Internet of Things, Service Oriented Architectures and Advanced Human Computer Interactions converge for more agile, flexible and proactive management of unexpected events in the global value networks of today. In essence, it concerns the adoption of Future Internet technologies in the frame of the Factory of the Future paradigm, for the virtual enterprise and its value network. Translating the same concept to production systems in manufacturing enterprises, and moreover to Smart Systems in general (smart manufacturing, smart cities, smart logistics,...), the capability by next generation systems sensing, modelling and interpreting the signals from the real world is a pre-requisite for a more flexible and agile reconfiguration of those smart systems. Intuitively, a sensing system requires resources and machineries to be constantly monitored, configured and easily interacted by blue collar workers. All these functions, and much more indeed, are now implemented by so-called Cyber Physical Systems (CPS). Cyber-physical systems (CPS) enable the physical world to merge with the virtual leading to an Internet of things, data and services. One example of CPS is an intelligent manufacturing line, where the machine can perform many work processes by communicating with the components. Using sensors, the embedded systems monitor and collect data from physical processes, like steering of a vehicle, energy consumption or human health functions. The systems are networked making the data globally available. Cyber-physical systems make it possible for software applications to directly interact with events in the physical world, for example to measure peaks in energy consumption. 

Web site: none
Code for submitting contributions: s674h
Full description: PDF
 

Co-creative Cyber Physical System in Smart Manufacturing and Logistics

Track proposed by:
Toshiya Kaihara, Tatsushi Nishi, Youichi Nonaka

Abstract:
Recent developments of IoT technologies have enabled the advancement of smart factory in Industry 4.0. Co-creation in manufacturing refers to the participation of stakeholders such as customers, suppliers, manufacturers, logistic providers in a product design or problem-solving to produce mutually valued outcomes. These outcomes include new things, new values, new services, new ways to overcome delivery problems or solutions to complex manufacturing and logistic problems. The adoption of this framework in smart manufacturing and logistics systems is, however, not straightforward. This open invited track aims to present recent advances on co-creative cyber physical system in smart manufacturing and logistics with rich objectives, and new challenges for smarter world. The topics covered here are closely related to the TC 5.2 research areas, such as the development of management decision-support systems in digital, resilient and sustainable manufacturing and supply chain systems, and we look forward to receiving many contributions to this track from the TC 5.2.

Web site: none
Code for submitting contributions: 33b84
Full description: PDF

Application of Machine Learning in Additive Manufacturing

Track proposed by:
Yaoyao Fiona Zhao, Ahmad Barari, Marcos de Sales Guerra Tsuzuki

Abstract:
Additive Manufacturing (AM) is a fast-growing field creating a new manufacturing philosophy. It has become a new paradigm for industrial manufacturing. AM results in a new manufacturing environment with very little geometrical and topological restrictions. Additionally, there is no need for manufacturing tool preparation. However, AM also brought forward the challenge of highly coupled material-design-process phenomenon. It is generally very difficult to model the mathematical relations of various correlated factors influencing materials, designs and process parameters. The high-fidelity physical-based models are too computationally heavy to provide fast and accurate results. Even though AM process is highly digitized, the digital models are stored in different format containing various levels of information at different dimensional and temporal scales. With the advancement of data acquisition and storage technologies, machine learning technologies have been increasingly adopted to discover hidden knowledge and build highly complex relationships in digital manufacturing systems. Highly digitized AM design, simulation and manufacturing chain supported with sensor networks could produce high volume data which creates a highly feasible condition to apply machine learning techniques for various purposes. This session provides an excellent forum for scientists, researchers, engineers and industrial practitioners to meet and share experiences, theoretical knowledge or case studies on the application of machine learning in AM. Authors are invited to submit full papers describing original research work associated with various aspects of machine learning application in AM such as data processing, machine learning model development, application examples in design, material development, process control.

Web site: none
Code for submitting contributions: bbnqn
Full description: PDF
 

Industry 5.0 - Human-Centered Production and Logistics Systems of the Future

Track proposed by:
Eric Grosse, Fabio Sgarbossa, Daria Battini, Christoph Glock, W. Patrick Neumann

Abstract:
This open invited track aims at investigating the development of innovative approaches for the integration of human factors in system design to create highly reliable and humanly sustainable production and logistics systems of the future. Topics may include, but are not limited to:
- Human-centricity in Industry 5.0 and Resilient Operator 5.0
- Opportunities to utilize human factors in Industry 4.0 for human-centered production and logistics systems
- Human factors in Logistics 4.0
- Technology adoption, reliability and maintainability
- Behavioral issues and the interactions of humans and new technologies in production and logistics
- The impact, chances and challenges of using technical assistance systems (wearables, AR, exoskeletons etc.) in manual industrial work
- Physical, cognitive and psychosocial human factors in operations and logistics management
- Learning and forgetting in industrial systems
- The impact of system design on human errors
- Reduction of injury risks in manual operations
- The impact of demographic changes / an ageing workforce on industrial system performance and safety

Web site: none
Code for submitting contributions: 84ju9
Full description: PDF

Advances Toward Smart Digitized Shopfloors

Track proposed by:
Yuval Cohen, Marco Macchi, Elisa Negri, Maurizio Faccio

Abstract:
The ongoing and incoming developments in technologies are nowadays radical and fostering relevant impacts  in key industrial processes. Industrial shopfloors are in perfect position to exploit the convergence of  digitalization and other technological advances in manufacturing and automation technologies. This will not  only improve shopfloor productivity and cost efficiency, but will also change the way manufacturing systems  operate and increase their product variety and customization. A new generation of smart, advanced and robust  systems are in various degrees of development – finally leading to smart manufacturing systems built upon  Industry 4.0 principles and technologies. The advances appear in various fields leading to the introduction of  augmented reality, machine vision and tracking, smart sensors and their fusion, machine learning and artificial  intelligence (AI), advanced smart robotics, cloud-edge computing capabilities, etc. Additionally, the Internet  of Things (IoT) enables smart manufacturing by offering connectivity of manufacturing systems, devices, tools,  products, and components.  
The challenge is to manage the transition toward smart manufacturing systems. Therefore, this Open invited  track focuses on the changes brought about by the technological advances in the industrial shopfloors. A major  question arises: “how to develop models and methodologies that best utilize the technological improvements  and instil effectiveness and foster efficiency in the shopfloors?” Considering this question as a major driver of  interest for this track, the utilization of optimization models, control algorithms and techniques, digitalization  and automation technologies, and management methods, is envisioned in order to allow smart cyber physical  manufacturing systems featuring self-optimization, self-configuration, self-diagnosis, and intelligent support to workers in their tasks. All these models, techniques, algorithms, methods and technologies would allow to  better employ cost-effective industrial shopfloor processes.  
Smarter operation of machines and shopfloors may utilize the advent of the digital twins to find ways for taking  full advantage of the virtual copy of the physical manufacturing process to enable quick and decentralized  decisions. Thus, better models will lead to significant improvement of flexibility and speed of the whole  manufacturing system.  
This Open invited track seeks original manuscripts in order to investigate the design and management of smart  manufacturing systems compatible with Industry 4.0 principles and technologies. It also seeks to exploit  mathematical models, algorithms and techniques, automation and digitalization technologies, management  methods and approaches as well as industrial case studies. A particular interest of the track is the development  of smart assembly, smart manufacturing, and smart part logistics, as well as the intelligent support systems for  manufacturing decision-making in the scope of these processes.  
Possible topics of this Open invited track include but are not limited to:  

- Intelligent support systems to assist workers in their increasingly complex tasks
- Augmented reality for operator assistance  
- Cobots and innovative robotic technologies and their implementations in shop floors
- Artificial intelligence for manufacturing processes  
- Computer vision systems for manufacturing processes  
- Big data analytics for manufacturing systems and processes  
- Machine learning for manufacturing processes  
- Digital twins for decision making in Industry 4.0 era  
- Virtualization and simulation techniques for manufacturing decision making  
- Bio-inspired manufacturing, theory of complexity, swarm intelligence, and self-adaptation
- Self-configuration and self-diagnosis IoT methods for manufacturing shop floors
- Self-optimization models for scheduling and sequencing manufacturing shop floor
- Blockchain technology and its application in manufacturing  
- Intelligent tracking and decision-making for resource efficiency in the circular economy
- Autonomy, autonomous vehicles, and drones  
- Control algorithms for smart part logistics  
- Smart part logistics design and management  
- Self-organizing systems and emergent behaviour  
- Novel case-studies of AI and smart technique integration in shopfloors  
- Smart assembly station and system design and management  
- Self-configuration and self-diagnosis methods and technologies for assembly systems  

Web site: none
Code for submitting contributions: 83ahi
Full description: PDF

Human Work and Skills Advances Related to Smart Manufacturing

Track proposed by:
Hila Chalutz Ben-Gal, Yuval Cohen, Chiara Cimini, Alexandra Lagorio

Abstract:
The work environment is changing. Some smart manufacturing work environments involve digitization, automation, machines and robotics, artificial intelligence, which replace human work (Autor, 2014). These changes influence smart manufacturing environments by reducing the demand for labor and wages (Acemoglu & Restrepo, 2018). We enter a new and dynamic industrial age in which machines and computers can substitute, complement and expand human work, requiring new and shifting human skills and competencies. Furthermore, two parallel trends continue to dominate the smart work setting. First, technology continues to govern how workers communicate and socialize (Ray & Thomas, 2019; Sela et al, 2022). Second, new work arrangements (e.g., freelance, gig-work, task, project-based work) are increasingly prevalent and are expected to reach a record high by 2030 (Barlage, van den Born, & van Witteloostuijn, 2019).
This rapid change has major implications on the human operator. Human operators in smart work environments, are incorporating information, data and implementing artificial intelligence more than ever before. We observe AI-based applications assisting workers in daily tasks, project management, decision-making and collaboration, thus enabling smarter time-critical tasks in industrial and managerial settings. As a result, emerges the need for new collaboration mechanisms, tasks and skills to support social interactions with peers, robots and artificial intelligent systems. Thus, the need to examine and evaluate the ever-changing human-centered perspective and required skills in order to make necessary adjustments. The shifting Smart Manufacturing setting, requires adjustments in skills and competencies in order to adjust to these rapid changes. New tools, frameworks and methods will be required support the human work in managing technology and digitized work streams. With these new developments, studies in the human-related factors should be carried out, on both the theoretical and practical levels, highlighting the interdependences between digital technologies and human skills, and providing high-tech and industrial firms with state-of-the-art tools to drive their workforce towards agile human-centred smart manufacturing environments.

Web site: none
Code for submitting contributions: 5gsv3
Full description: PDF

Digital Advances in Latin America in Asset Management Practices: Pilots and Case Studies

Track Proposed by:
Giacomo Barbieri, Orlando Durán, Luca Fumagalli, Carlos Eduardo Pereira, David Romero

Abstract:
Latin American economy has mainly been an export-based economy. Due to its large areas of land that are rich in minerals and other raw materials, mining has traditionally been the main economic sector. However, the region is characterized with other sectors that are already significant in the worldwide landscape such as the food industry in Brazil, and the automotive manufacturing in Mexico, amongst others. Furthermore, other sectors have the potential to grow notably in the future. For instance, due to its vast geography and abundant natural resources, this region may become a major player in the field of renewable energy. Adoption of new technologies in Latin America has traditionally been delayed with respect to other more industrialized regions. This phenomenon is also occurring with digital transformation technologies. For this reason, this session provides an excellent forum for scientists, researchers, engineers and industrial practitioners to meet and share successful case studies and lesson learnt from the application of digitalization technologies to the asset management in Latin America. Since asset management include all the life cycle activities of physical assets, contributions concerning the digitalization of maintenance and operations are also welcome. 

Web site: none
Code for submitting contributions: qmd79
Full description: PDF

Intelligent Methods and Tools supporting Decision Making in Manufacturing Systems and Supply Chains

Track Proposed by:
Michael Freitag, Raphael OGER, Enzo Morosini Frazzon, Carlos Eduardo Pereira

Abstract:
This Open Invited Track is concerned with the development of decision support systems and approaches for managing manufacturing systems and supply chains. Systems and approaches can be inspired from combinations of tools and approaches from industrial engineering, operations research, decision science, computer science, and data science.  
The Volatility, Uncertainty, Complexity, Ambiguity (VUCA), and diversity of manufacturing system and supply chain environments lead to an increasing complexity for decision makers to make decisions. So, decision makers need to be supported by proper decision-making models, methods, and software tools. Such systems can be for example based on simulation, optimization, data analytics, machine learning, what-if analyzes, ontologies, taxonomies, or combinations of these. At the same time, these systems are connected to sensors for data acquisition and as well as communication systems to exchange data and information both in vertical and horizontal direction. The proper exchange of data between the physical process and intelligent systems allows for the emergence of adaptive, agile and resilient manufacturing systems and supply chains.   
The track chairs invite scientists, engineers and decision makers from government, industry, and academia to contribute with theoretical and applied research papers. From the business perspective, the track will cover different activities such as design, planning, scheduling, control, monitoring, and maintenance for supply chains, production, transportation, logistics, inventory, and warehouse. From the decision support perspective, the aim of this track is to attract high-quality papers contributing to these business activities by proposing new decision support systems and approaches. In the context of a highly uncertain and opportunistic world, a special attention will be given to paper contributing with new decision support systems and approaches contributing to risk/uncertainty and opportunity management. A special attention will also be directed towards practical relevance and approaches that can foster innovation in manufacturing and supply chains.

Web site: none
Code for submitting contributions: 8316w
Full description: PDF

Machine-Learning Techniques in Predictive Maintenance

Track Proposed by:
Paulo Gil, Alberto Cardoso

Abstract:
Recent advances in manufacturing have been driven by the so-called Industry 4.0 paradigm and a model of production centred around the concept of circular economy. These two driving forces, altogether, have fostered the integration between physical and digital environments and people. As a result, large amounts of data containing information about the underlying processes, such as physical variables, states, and alarms, just to name out a few, are collected in a distributed way and shared among different players and devices.  
Once data are available, relevant information about systems’ status can be extracted by applying data-driven approaches, namely, Machine Learning (ML) techniques. Given their inherent features, ML-based methodologies have been widely considered in condition-based maintenance (CBM) problems, aiming to guarantee and improve the availability of manufacturing systems and targeted throughputs, while reducing maintenance costs and promoting sustainability. Knowing the health condition status of a set of components of a given system is invaluable to support optimal scheduling of Predictive Maintenance (PdM) tasks. This Special Session/Track intends to bring together researchers, engineers, and practitioner communities, to present and discuss the latest advances and challenges on ML-based PdM, with focus on data gathering, data processing and decision-making, including but not restricted to: 
 - Architectures for PdM
 - Algorithms for PdM
 - Sensing and cyber physical systems in PdM
 - Condition monitoring
 - Fault detection, prognosis, and Remaining Useful Life (RUL) estimation
 - Maintenance as a service and reliable manufacturing
 - Industry applications and case studies

Web site: none
Code for submitting contributions: v43b8
Full description: PDF

Human in the Loop of Artificial Intelligence in Smart Maintenance and Manufacturing Systems

Track proposed by:
Jon Bokrantz, Christos Emmanouilidis, Paulo Leitão, Jože Martin Rožanec, Thorsten Wuest 

Abstract:
AI-driven human augmentation or industrial automation have seen many applications in maintenance and manufacturing. High expectations are set regarding AI-driven solutions and automated outcomes, but the role of the Human in the Loop in producing these outcomes is less well explored. This is surprising given that Human integration in Sociotechnical Systems has long been studied. Much is expected to be achieved in automated manner from Machine Learning in industrial systems, leaving the possibility for properly integrating human knowledge and human capabilities insufficiently exploited. Yet, the application practice of Machine Learning and broader AI in Maintenance and Manufacturing provides ample evidence of brittleness of derived solutions in the face of limited or new data, changing contexts, or evolving situations. AI communities seek to address such challenges with approaches such as Transfer Learning. More recently Active Learning has been explored to better focus on the integration of Human Interaction, and therefore the Human Physical and Cognitive Capabilities in the AI Loop. The interest in addressing Human – Centricity in Industry 5.0 often targets high conceptual, abstraction, and design levels, and does not sufficiently target the interactive and operational engagement of the Human in the AI Loop. This pattern is changing, especially in domains with high performance, safety or ethics requirements, with research targeting mixed or sliding autonomy and decision making, shared contexts and collaboration workspaces. Such approaches deserve further research in maintenance, as well as manufacturing shop floor contexts. The simplest cases are human – annotated data to drive machine learning. Modelling and integration of domain knowledge with data via knowledge graphs and ontologies is also pursued. Employing human operators, workers, or engineering staff as a source of observation, knowledge, decision, or action is another example. The collaboration of human and non-human (AI-driven) cooperating agents in industrial systems is a further step. Considering the above, the effective integration of humans in the Machine Learning and Broader AI Loop for Maintenance and Manufacturing is the focus of this track.

Web site: none
Code for submitting contributions: 24qtf
Full description: PDF

Fault Diagnosis, Prognostics and Health Management for Dynamic Systems based on Data-centric Methods

Track proposed by:
Chen Lu, Minvydas Ragulskis, Laifa Tao, Yu Ding

Abstract:
Accurate and real-time state cognition for complex dynamic systems is the key to ensuring the normal operation of the system, reducing the accident rate and maintenance costs. Thus, how to realize Prognostics and health management (PHM) efficiently has become one of the most attractive topics in both academia and industry. Complex dynamic systems such as control systems, power systems, electromechanical systems, and electronic systems in manufacturing industries are keen to apply PHM to predict, diagnose, monitor, and manage the state or condition of engineering assets.  The overarching intention of this Open invited track is to publish new approaches dealing mainly (but not exclusively) with those state-of-the-art methods of signal processing, autonomic feature extraction, health assessment and diagnosis, and performance degradation prediction, health management strategy optimization. Emphasis will be focused on various leading-edge theories and methodologies, such as deep learning, digital twin, adversarial learning, and evolutionary game theory, which are expected to address the existing challenges for a real-world PHM system. If deemed relevant, integration techniques of diagnosis and prognostics-oriented maintenance strategy optimization can also be presented. This Open invited track aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in detecting anomalies, forecasting potential degradation, and classifying faults by monitoring and analyzing signals collected from different complex dynamic systems, as well as proposing a new framework for maintenance strategy, automatically optimizing resources for health management. Prospective authors are invited to submit high-quality original contributions for this Open invited track, including Regular papers, Survey papers, or Discussion papers.

Web site: none
Code for submitting contributions: d6i7g
Full description: PDF

The ‘Rocky Road’ towards Industry 5.0: Achieving Human-centricity in AI-enabled Industrial Manufacturing Work Environments and Systems

Track proposed by:
Eva Coscia, Christos Emmanouilidis, Marco Macchi, David Romero, Johan Stahre, Sabine Waschull

Abstract:
To overcome the overly “techno-centric” transformation towards Smart Manufacturing, Industry 5.0 promotes human-centricity, resilience, and sustainability. Human-centricity places humans and their needs/interests at the centre of manufacturing systems, facilitating a human-technology symbiosis where the interaction enhances the capabilities of all system actors involved (i.e., technical and human) and enables new cooperation models. This attains more importance and criticality, given the further inclusion of AI-driven systems within Digital Twin frameworks for Systems and Humans. Integration between humans and AI in manufacturing is expected to change the nature of both blue- and white-collar work and even blur boundaries between them. Evolving research avenues target requirements of human-centric designs in AI-enabled environments and different technical/social choices that enable such designs, considering also ethical and legal implications. Introduction of AI in shopfloor and workspaces drives the creation of new human-machine interaction modalities with shifts of responsibility and revision of roles. This gives rise to new risks, such as reduced sense of responsibility by human operators, new social dynamics among colleagues and incorrect perception of AI cognitive limits and possibilities of failures. Instead of a narrow viewpoint of human-technology interaction, often focusing on singular work design criteria, an overall work system of humans’ design perspective is needed. This involves different organisational decision-makers and functions with varying backgrounds, expertise, and mindsets that need to cooperate, thus relevant organisational issues need to be explored. New methods and frameworks are needed to identify design criteria based on sound empirical studies addressing different use-cases and implications for human work. Therefore, the session invites researchers to submit conceptual and empirical papers including reviews, case studies, design-oriented papers including frameworks/methods/simulations, and real examples from AI-driven manufacturing implementations, leading to clearer conceptualization and effective application of human-centricity in AI-enabled manufacturing (details in attached PDF)

Web site: none
Code for submitting contributions: v66fr
Full description: PDF

Simulation modeling, machine learning and optimization algorithms to support decision making in production and logistics

Track proposed by:
Tobias Reggelin, Stefan Galka, Lorena Silvana Reyes Rubiano, Sebastian Lang, Nasser Mebarki, Mona Wappler

Abstract:
Enterprises still make a lot of decisions in production and logistics based on simple rules or the individual know-how of the decision-makers. The same applies for public/governmental authorities for decisions related to public logistics systems. The use of simulation modeling, machine learning, and optimization algorithms can lead to drastically better decision making in enterprises and authorities. The ongoing digitization, the pursuit of concepts related to Industry/Logistics 4.0, further increasing computational power and more and more well-educated employees in enterprises and authorities provide an excellent basis for the application of the above-mentioned models to support decision making in enterprises and authorities. For this reason, this track focusses on all kind of models related to simulation, optimization and machine learning and their applications to support both real-time operational decisions and middle/long-term planning decisions in production and logistics which go beyond the state of the art and concepts for application-oriented teaching of these topics in academia and practice.  
The track chairs invite researchers and decision makers from academia and industry to contribute theoretical and applied research papers in areas including but not limited to the following topics: 
Microscopic, mesoscopic, macroscopic, hybrid, and adaptive simulation models. Models from the field of AI, e.g. machine learning. Optimization heuristics. Real-time operational decision making, tactical decision making, and strategic decision making in production and logistics. Digital twins for planning and control of processes in manufacturing, logistics and supply networks. (Re)configuration of supply networks. Virtual commissioning. Assistance systems. Location and transport optimization. Urban and sustainable logistics systems. Standardization of Data Models for Digital Twins. Data-driven and model-driven simulation. Modeling of energy consumption in manufacturing and logistics systems. The track chairs also invite lecturers from academia and industry to present new educational concepts for application-oriented teaching of simulation modeling, optimization and AI with application in production and logistics.

Web site: none
Code for submitting contributions: xs225
Full description: PDF

Large-Scale Complex Networked Systems: Analysis and Control

Track proposed by:
Xiaofan Wang, Ming Cao, Wei Ren, Hans Rauno Mikael Aalto, Lin Wang

Abstract:
In the past two decades, there have been more and more research interests in the analysis and control of large-scale complex networked systems, with application to communication networks, power grids, transportation networks, biological networks, social networks and so on. The aim of this track is to bring together different communities working on different aspects of complex networked systems. The track will discuss some fundamental issues on control of complex networks, including controllability and observability of complex networks, mathematical and algorithmic tools for analysis and design of large-scale networked systems, large-scale complex systems under cyber, physical or social constraints, and potential applications to real-world systems.

Web site: none
Code for submitting contributions: 9d8tb
Full description: PDF

REMANUFACTURING for CIRCULARITY concern

Track proposed by:
Fabio Fruggiero, Angelos Markopoulos, Mozafar Saadat, Mario Caterino, Yassine OUAZENE

Abstract:
The technology “across” processes of Industry 4.0 is now addressing the flexibility of production and the robustness of value chains to gain circularity and sustainability through technology. It integrates the Human-Centric perspective asking to apply technologies to adapt production processes to the societal and environmental and industrial landscape. This involves designing (and promoting) products (and processes) that can be reused and repaired (Re-Manufactured) in a sustainable circular economy perspective. Remanufacturing as the series of steps needed to transform an old (broken) product into one to be considered as new (repaired) [BS8887-220:2010] involves: market analysis for products collection; quality issue for initial inspection and market re-entering and tracing; process arrangement for the disassembly and reassembly balancing; product requirements for modularity, fast prototyping and remediation of components; logistics issues for the reverse and warranty management.  The remanufacturing and refurbishment market is rapidly (e.g., in the global automotive parts remanufacturing market grows with a CAGR of 7.1% over the forecast period from 2020-2026 but still account 2% of US production and 19% of EU production) gaining a huge market amount (it is expected 100 Bn of euros for the European market). Considering the complexity or remanufacturing, product and process and company barriers have to be overcome while discussing (and testing) remanufacturing strategies (and approach) for circularity.   
The objective of the this proposed section is to collect contributions to share knowledge on: business model to support integration, identification, application of I40 technologies in remanufacturing and refurbishment; model, design, control of processed to overcome uncertainty in timing and quality of returns, balancing of load;  design and arrangement  for reverse  - service oriented - logistics ; manufacturing strategies for part integration and specificity making; cost barriers for remanufacturing development; customer points for remanufacturing and circularity; human and robot cooperation for remanufacturing; deep sustainable maintenance strategies for remanufacturing.

Web site: none
Code for submitting contributions: 4khj5
Full description: PDF

Challenges and opportunities in applying Additive Manufacturing in Supply Chains 

Track proposed by:
Mirco Peron, SERENA FINCO, Francesco Lolli, Nils Knofius, Fabio Sgarbossa, Dmitry Ivanov, Tsan-Ming Choi

Abstract:
Additive Manufacturing (AM) has recently emerged as a disruptive manufacturing technology since it allows to produce in a fast and simple way parts with a level of complexity not even imaginably achievable with conventional manufacturing (CM) techniques. Aerospace and automotive sectors were the pioneers in exploiting AM, and its use has now expanded. After-sales companies, for example, have been attracted by the possibility provided by AM to reduce the stock levels by producing spare parts on demand. Furthermore, the possibility of AM to produce parts on the site of use has repercussions not only on the production phase, but on the whole supply chain: the supply chain complexity can be decreased, as well as its environmental footprint, while its responsiveness can be highly increased. This is particularly important considering the current uncertain times (Covid-19 pandemic, wars, natural disasters, …). In such a context, AM can increase the resilience and viability of supply chains, as well as support humanitarian organizations in the first recovery phase.  However, despite all these potentialities and benefits, AM’s deployment is still limited due to some main limitations. Firstly, AM parts are more expensive than CM counterparts. This is further exacerbated if parts need to be qualified according to the different standards (ISO, ASTM, …). Moreover, the still high investments necessary for buying an AM machine limit the cases where producing parts on-site is economically convenient. Furthermore, recent years have seen an increasing number of legal actions for Intellectual Property (IP) infringement when using AM parts. Finally, some dilemmas arise also from an environmental perspective: on the one hand, AM allows to reduce the environmental footprint by producing parts on-site, but, on the other hand, the AM production process is very demanding in terms of energetic consumptions. It is hence still not clear when it is convenient to adopt AM, and this becomes even more uncertain if we include in the analysis the risks of disruptions: the adoption of AM might lead to higher costs, but it might ensure the viability of the supply chain. This session aims at solving these dilemmas.

Web site: none
Code for submitting contributions: p4t41
Full description: PDF

Supply Chain Resilience and Viability

Track proposed by:
Daria Battini, Dmitry Ivanov, Alexandre Dolgui, Martina Calzavara

Abstract:
Enterprises worldwide are facing significant challenges from growing competition and the destabilizing effects of climate, disease, and other external perils. Supply chain management (SCM) has been recognized as a critical capability to navigate such risks successfully with the aim of design resilient and reliable Supply chains (SCs) marked by a high capability of withstanding and recovering rapidly from disruptive event. In last decades, researchers have addressed various facets of supply chain risk management like risk identification, assessment, mitigation, and monitoring. However, the recent example of coronavirus COVID-19 outbreak demonstrates the necessity to extend the angles of SC resilience and risk management towards new paradigms such as SC viability (the system ability to meet the demands of surviving in a changing environment). This Open Invited Track aims to investigate the development of innovative approach in the field of supply chain risk management from a multi-disciplinary operational perspective. The aim of this session is to attract high-quality papers detailing the most recent developments in the field of tackling uncertainties, building resilience, and developing adaptable systems in supply chains. An important area of interest is the development and integration of new KPIs to be considered for future supply value chain designs based on resilience, responsiveness, and re-configurability apart from the more traditional metrics such as cost, quality and delivery, and new performance measures.  The track chairs invite scientists, engineers and decision makers from government, industry, and academia to contribute theoretical and applied research papers in areas including but not limited to the following topics: Supply chain design with resilience and business continuity considerations, Trade-off  “resilience vs. efficiency” in redundancy/robustness planning, Creating flexibility and adaptability in the supply chain, Role of Operational Research in the future epidemics/ pandemics, Development of KPIs to be considered for future supply value chain designs (e.g. resilience, responsiveness, reconfigurability), Quantitative analysis techniques for supply chain control, optimization, simulation. 

Web site: none
Code for submitting contributions: p1rc6
Full description: PDF

Recent advances in Smart and Sustainable Manufacturing and Maintenance

Track proposed by:
Malgorzata Jasiulewicz-Kaczmarek*, Katarzyna Antosz, Arkadiusz Gola, Patrik Grznar, Robert Cep, José Machado, Chao Zhang Chao Zhang

Abstract:
Increasing of  globalization and contemporary market demands, manufacturing efficiency is more and more often defined not only as the capacity for low-cost production of specified quality products but also being in line with social and environmental requirements. In this context the manufacturing and maintenance functions play an important role, which ensure the asset availability and product quality simultaneously monitor of natural resources using and people and process safety. On the one hand, the effectiveness of manufacturing and maintenance functions guarantees the compliance of processes and products and reduces their impacts on society, and the surrounding environment. On the other hand, they must be sustainable business functions by themselves and limit their flows and impacts generated during all production and maintenance activities. Moreover, Industry 4.0 technologies bring high potentiality for enhancing manufacturing and maintenance management and execution, which is an opportunity for integrating the sustainability dimension. Adopting sustainability in manufacturing and maintenance requires a comprehensive look covering not just these processes but also the technologies involved in their realization.  The objective of this Open Invited Track is to present the state-of-art of theoretical developments and applications of Industry 4.0 technologies in the area of sustainability in manufacturing and maintenance areas in various industry sectors. This Special Session is focused on:  • optimal and sustainable manufacturing systems designing,  • designing and improving of smart and sustainable manufacturing and maintenance processes,  • applications of Industry 4.0 technologies for modelling sustainable manufacturing and maintenance systems and processes,  • implementing interoperability in real and cyber-physical manufacturing and maintenance systems. 

Web site: none
Code for submitting contributions: kc5ft
Full description: PDF

Sustainable Manufacturing: Energy management stakes in a digitized world

Track proposed by:
Maroua Nouiri, Adriana Giret, Elisa Negri, Olivier Cardin

Abstract:
Manufacturing companies are known by their relevant resource and energy consumption and negative impact on the environment. Reducing emissions, waste and energy consumption are one of the main objectives of many industries. This session addresses sustainability as a huge challenge in manufacturing and highlights in particular energy management stakes.  The vision of the fourth industrial revolution is not only to optimize production and minimize costs but also to control the consumption and cost of energy in manufacturing with the new opportunities opened by digital technologies, artificial intelligence (AI), and the use of data analytics.  A significant change in Industry 4.0 compared to the old traditional industries is the fusion of the physical and the virtual worlds, which is made possible by Cyber Physical Systems (CPSs) and Internet of Things technologies (IoT). Indeed, typical resources (machines, operators…) are converted into intelligent objects and smart humans so that they become able to sense, act, and interact within a smart connected environment.  Exploiting their communication capabilities allows to obtain a collaboration mechanism achieving an energy-efficient control system. Meanwhile, the production system shall be stable and robust towards all kinds of disruptions, whether internal like machine failures or random jobs arrival, or external such as dynamic variation in energy availability, different prices of energy sources, etc.  This session aims at bringing together reflections and innovative ideas on methodologies and solutions to manage energy consumption and to get energy-efficient manufacturing systems through the use of advanced industrial engineering approaches and technologies, such as innovative Multi-Agent Systems (MAS) architectures, AI techniques, digital twins, to name a few. It is important to consider energy management and efficiency from the design stage of intelligent control tools, to the design of tools to sense in real time the energy consumption of manufacturing activities and to the analysis of operational data to predict the future consumption. 

Web site: none
Code for submitting contributions: b8d3u
Full description: PDF

Implementing Digital Twin in Manufacturing and Logistics Systems: New Trends and Challenges

Track proposed by:
SERENA FINCO, Mirco Peron, Audrey Derrien, Olga Battaïa, Xavier Delorme, Daria Battini, Dmitry Ivanov

Abstract:
Digitalization in Manufacturing and Logistics (M&L) systems represents a crucial driver able to lead companies in achieving higher levels of productivity and flexibility. Digital technologies, such as Internet of Things, Cloud Computing, Artificial Intelligence, Virtual Reality, Augmented Reality, or the new generation of Information Technologies, allow an intelligent integration and interconnection among all actors involved in the M&L processes. Moreover, digital technologies enable real-time monitoring, control, and data collection, as well as the development of Cyber-Physical systems that are able to combine the physical and virtual environment. In this context, the Digital-Twin (DT) concept represents an emerging research topic in M&L systems. DT is defined in several ways according to the application field. However, in M&L systems, the DT consists in creating a virtual representation of the system aiming to evaluate, predict, and optimize their states and future behaviour. Moreover, the data flows between physical and virtual entities are integrated in both directions. It implies that a change in the physical entity could lead to a change in the virtual one and vice versa. In such a way, DT is designed to continuously elaborate data from the past, monitor the present in real-time, and, finally, support future operations decision-making by combining past data with the real-time ones. According to the scientific literature, DT is applied in several M&L phases such as production planning and control, workpiece quality prediction, machine and human-robot collaboration, real-time M&L systems monitoring, product traceability, and performance prediction, supply chain resilience. However, there are some critical issues in implementing DT. For example, there are difficulties in sharing DT in multiple application systems involving several stakeholders, difficulties in ensuring efficient storage, processing, and analysis of a large amount of data. Furthermore, due to the high degree of heterogeneity in digital technologies, there are some difficulties in making suitable decisions and investments in DT from a company perspective. This session aims at solving these dilemmas. 

Web site: none
Code for submitting contributions: 27r51
Full description: PDF

Advanced Prognostics and Health management for manufacturing systems in the era of Industry 4.0

Track proposed by:
Thi Phuong Khanh Nguyen, Kamal Medjaher, Joo Ho Choi

Abstract:
In recent years, the widespread deployment of internet of things (IoT) technology within manufacturing assets has facilitated the upgrade and transformation of manufacturing sectors towards the factory of Industry 4.0. In this context, the increasing requirements for the availability and reliability of complex production assets as well as the high safety of the working environment and reduction of maintenance costs have become important goals. One of the solutions to achieve this goal is to develop advanced Prognostics and Health Management (PHM) solutions. They include development of methods, algorithms, and tools that continuously monitor systems to early detect abnormal operating modes, diagnose probable causes, anticipate failures, and take appropriate decisions accordingly.  In practice, the important growing of intelligent manufacturing systems complexity poses many challenges for PHM functionalities. Indeed, analyzing multiple heterogeneous sensor sources requires new powerful methods for acquisition, storage, fusion, and online processing of big data. Complex system structures and behaviors need to be studied, analyzed, and modeled in a thoughtful and intelligent way. The merging of digital and physical worlds leads to an increasing number of options to weigh in the optimization of the entire production and maintenance strategy. Therefore, this session aims to provide researchers and industrial experts and practitioners the opportunity to present and discuss recent solutions for the above-mentioned issues, as well as to share new perspectives.  The topics of interest include, but are not limited to: 
• Data acquisition, processing, fusion and analysis of heterogenous data,  
• Multimodal learning for PHM,  
• Physics informed machine learning for PHM,  
• Condition monitoring, fault detection, diagnostics, and prognostics,  
• Degradation modeling, health assessment, and remaining useful life estimation,  
• Condition-based and predictive maintenance,  
• Case studies on prognostics and health management.

Web site: none
Code for submitting contributions: a9f8t
Full description: PDF

Proactive-reactive methods of maintaining production and development in crisis periods

Track proposed by:
Justyna Patalas-Maliszewska, Grzegorz Bocewicz, Marcin Mrugalski, Arkadiusz Gola, Zbigniew Banaszak, Izabela Nielsen, Damaševičius Robertas, Matthias Rehm

Abstract:
The increasing adaption of methods from computational intelligence, biologically inspired computing, and optimization in engineering disciplines such as mechatronics, production or logistics enables the development of a new class of approaches allowing to prototype the proactive-reactive operation scenarios of manufacturing systems. This kind of scenarios are distinguished by the endogenous adaption of objectives as reaction to changing influences and the resulting autonomous adjustment of parameters or structure and consequently of the system‘s behavior. The objective of this Open Invited Track is to is to develop crucial possible scenarios for self-healing company in the in cases of the unexpected occurrence of the crisis and next, using artificial intelligence methods and tools to build intelligent simulation models showing possible scenarios of conduct in crisis situations under given conditions. In that context it focuses on:  
- the process of a fully automated acquiring data, information and expert knowledge within a manufacturing, then formalizing and transferring it. 
- building and application of data-based models for data intelligence in production. 
- simulation models showing possible scenarios of conduct in crisis situations under given conditions. 
- modeling and implementing interoperability in the cyber-physical manufacturing systems.

Web site: none
Code for submitting contributions: 92cnx
Full description: PDF

Advanced Soft-Sensor Systems for Fault Diagnosis, Process Monitoring, Control, and Optimisation

Track proposed by:
Yuri A.W. Shardt, Kevin Brooks, Xu Yang, Sanghong Kim 

Abstract:
As process become more complicated and the regulatory constraints more strict,  there is an increasing need to understand the processes. Unfortunately, accurate,  online measurements of many of the required variables for control, monitoring, and optimisation may not be feasible at the sampling times required. Thus, there is a need to develop and implement advanced soft sensors that can provide forecasts about the current state of the system using the available information. This invited  session will provide the opportunity for both academic and industrial researchers  to exchange their ideas and thoughts with the goals of finding common problems  and solutions. 

Web site: none
Code for submitting contributions: 9p7cw
Full description: PDF

The State of the Art in Flotation Modelling and Control

Track proposed by:
Kevin Brooks, Derik le Roux, Lidia Auret 

Abstract:
Flotation is one of the key unit operations in mineral processing. While progress has been made in the modelling, control and optimisation of flotation processes, there remain many opportunities to improve the efficiency and economics of flotation circuits. Model predictive control for flotation is an area of current research both in academia and the industry. This invited session will provide the opportunity for both academic and industrial researchers to exchange their ideas and thoughts to find common problems and solutions.

Web site: none
Code for submitting contributions: 81j53
Full description: PDF

Sustainable Transportation and Energy Systems: Automation and Optimization

Proposed by:
Luca Parodi, Giulio Ferro, Massimo Paolucci, Michela Robba, Mariagrazia Dotoli, Yrjö Majanne, Yassine Ennassiri

Abstract:
Sustainable transportation and energy systems have gained mauch more interest as key technologies to attain a considerable reduction in the greenhouse gas emissions. Nowadays, Electric Vehicles (EVs) became widely commercialized, and used by customers, and Charging Stations (CSs) are being continuously implemented to satisfy the increasing demand. Moreover, energy systems including renewables, non-renewables, hydrogen systems, CSs and distributed generation are being integrated more and more into energy communities. In this framework, new optimization methods and control approaches, technologies and ICT platforms are required for the integration of sustainable transportation and energy systems, as well as for users’ management and involvement; these, all together, form the main focus of this Open Invited Track. In recent literature, it has been shown an increasing and significant interest in the use of optimization models for the location of charging stations, charging operations (considering centralized and decentralized approaches), business models and EVs integration in smart grids, microgrids, biomass supply chain design for biodiesel production, hydrogen production management, harbors’ electrification and electrical public buses management. Distribution Systems Operators, managers of cities and communities, companies in the transportation sector, and providers of charging stations and energy services are now investing in sustainable mobility through the development of new ICT platforms, tools and technologies for the integrated optimal management of transportation and energy systems.

The proposed Open Invited Track aims to collect new advances in optimization and control approaches in transportation and energy systems, both from an application and methodological point of view.

Web site: none
Code for submitting contributions: qcxsg
Full description: PDF

Advanced Evolutionary Computation for Operation and Planning in Distribution Networks

Track proposed by:
Hiroyuki Mori, Hirotaka Takano

Abstract:
This open invited track provides new methods for distribution　network　operation and planning with Advanced Evolutionary Computation. In recent years, distribution networks are faced with a lot of uncertainties due to the emergence of Renewable Energy, EV Charging/Discharging, Power Markets, and DR under Smart Grid circumstances. As a result, distribution network operation and planning become much more complicated so that the mathematical formulations result in nonlinear optimization problems with uncertainties. Evolutionary Computation repeatedly makes use of simple rules or heuristics to evaluate highly approximate solutions to a global minimum in nonlinear optimization problems. However, the conventional Evolutionary Computation algorithms such as PSO (Particle Swarm Optimization) and its variants do not necessarily provide good solutions due to the influence of the initial solutions on the final ones in power systems. There is still room for improvement in solution accuracy. Thus, it is necessary to develop the Advanced Evolutionary Computation algorithms which are better than PSO and its variants. Also, well-thought-out approaches are required to deal with uncertainties.

Web site: https://hmori2911.wixsite.com/tc6-3-oit
Code for submitting contributions: h29pm
Full description: PDF

Applications of Advanced Deep Neural Networks to Forecasting Problems in Smart Grid

Track proposed by:
Hiroyuki Mori, Shoichi Urano

Abstract:
This Open Invited Track presents new Deep Neural Networks (DNNs) for forecasting problems in Smart Grid. It is aimed at understanding different DNN models from a standpoint of theoretical background and the applications in Smart Grid.  It has important components like Renewable Energy, Power Markets, EVs, DR (Demand Response), Energy Storage Systems (ESSs), Virtual Power Plants (VPPs), etc.  As a result,  Smart Grid operation and planning become much more complicated and highly nonlinear. Turning our attention to Artificial Intelligence (AI), it is now the third AI boom since the development of Deep Learning in 2006. The use of DNNs has been rapidly spread in the fields of engineering fields due to better performance than the conventional Artificial Neural Networks (ANNs) in terms of model accuracy. On the surface, DNNs might look similar to ANNs, but in practice, DNNs are often different from conventional ANNs. DNNs have been originally developed in the field of image processing, which means that it is not necessarily straightforward to apply DNNs to smart grid operation and planning.  Namely, we need to think out new applications by having enough knowledge about DNNs in different case studies. In this invited open track, we focus on forecasting problems in Smart Grid.

Web site: https://hmori2911.wixsite.com/tc6-3-oit
Code for submitting contributions: f3u93
Full description: PDF

Modelling, Control and Optimization on Wind Energy

Track proposed by:
Jesus Enrique Sierra-Garcia, Matilde Santos, Hugo Diaz, Ye Li

Abstract:
The aim of this track is to bring together research works on different aspects of wind energy systems, mainly focused on modelling, control and optimization of onshore and offshore wind turbines. The track will discuss some issues of these complex systems, including application of conventional and soft computing techniques, heuristic optimization, intelligent approaches, both for system modelling and control design, and potential applications to real-world systems.

Web site: none
Code for submitting contributions: di68k
Full description: PDF

Optimal control and control-oriented modelling of wave energy conversion systems

Track proposed by:
Nicolás Faedo, Siyuan Zhan, Bingyong Guo, John Ringwood

Abstract:
The pathway towards efficient exploitation of the vast energy available in ocean waves is inherently linked to suitable control technology. In particular, effective commercialisation of wave energy conversion systems (WECs) strongly depends upon the availability of tailored controllers able to maximise energy extraction from the wave resource, while minimising risk of component damage. The control problem for WEC systems naturally lends itself towards optimal control theory, where the control objective is, effectively, optimal energy capture, subject to a set of device-dependent physical limitations. Not only such a control objective can often lead to non-convex solution spaces, but achieving optimality depends upon future knowledge of external uncontrollable inputs, which renders this problem inherently non-causal, further departing from traditional tracking/regulation objectives. This open invited track intends to gather novel state-of-the-art strategies from the field of system dynamics and control, providing innovative and efficient solutions to the WEC control problem, with the potential to greatly contribute in the path towards enabling effective exploitation of the vast wave energy resource available.

Web site: none
Code for submitting contributions: 8hm8u
Full description: PDF

Aerospace Industrial Benchmark on Fault Detection and Fault Tolerant Control

Track proposed by:
Philippe Goupil, Jacobus Adriaan Albertus Engelbrecht, Simon Oudin, Masayuki Sato, Daisuke Karikawa

Abstract:
Fault Detection (FD) and Fault Tolerant Control (FTC) are important topics in the aerospace industry as well as in academia, and thus many research projects and programmes have been conducted in the last two decades. To promote this movement, an open invited track related to FD and FTC focusing on FD and FTC in flight control of civil aircraft is proposed for the IFAC World Congress 2023. This open invited track is for a competition to use FD and FTC techniques with a benchmark problem, similar to the “Aerospace Industrial Benchmark on Fault Detection"" competition at the IFAC World Congress 2020 in Germany. 

Web site: https://www.ifac2023.org/program/competitions/aerospace-industrial-benchmark/
Code for submitting contributions: ma3g7
Full description: PDF

Control and Optimization of Smart Grids Integrated with Renewable Energy Sources

Track proposed by:
Kwang Y. Lee, Jaeseok Choi

Abstract:
With the growing penetration of Renewable Energy Sources (RESs), such as wind and solar, into the electric power grid, overcoming increasing intermittency and unpredictability and conserving electric power grid resiliency under reduced inertia conditions have become serious issues. To solve these problems, Energy Storage Systems (ESSs) need to be integrated with the power grid. There are several ESS concepts developed over the years, such as Battery Energy Storage, Flywheel Energy Storage, Hydrogen Energy Storage, Pumped Storage Hydropower, etc.   Some of these have been around for many years and are mature, however, they may not be scaled to suit the needs of large-scale power systems and economically viable. Moreover, they still need to improve the various ancillary services it can provide to the electric power grid, such as frequency control, reactive power control, etc.   
The purpose of this Open Invited Track will focus on the development and application of modeling, control, optimization, and simulation tools to study the reliable power system, grid modernization based on artificial intelligence (AI) algorithms, and resiliency improvement of power system under the uncertainty of increasing penetration of RESs. 

Web site: none
Code for submitting contributions: bbvvd
Full description: PDF

Modern Heuristic Optimization Methods in Smart Grids

Track proposed by:
Kwang Y. Lee, Yrjö Majanne

Abstract:
Heuristic search and optimization is a new and modern approach for solving complex problems that overcome many shortcomings of traditional optimization techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied to many types of objective functions and constraints. Developing solutions with these tools offers two major advantages: development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information known as uncertainty.  
In competitive electricity market along with increasing penetration of renewable energy sources, heuristic optimization methods are very useful. As electric utilities are trying to provide smart solutions with economical, technical (secure, stable and good power quality) and environmental goals, there are several challenging issues in the smart grid solutions such as, but not limited to, forecasting of demand, weather, price, ancillary services; penetration of distributed and renewable energy sources; bidding strategies of participants; power system planning & control; operating decisions under missing information and big data; increased distributed generations, energy storage systems, and demand response in the electric market.   
The objective of this Open Invited Track is to review the state-of-the-art technologies in the modern heuristic optimization techniques and present case studies how these techniques have been applied in smart grids. 

Web site: none
Code for submitting contributions: r56vd
Full description: PDF

Wind turbine and wind farm control: Control challenges and solutions

Track proposed by:
Jan-Willem van Wingerden, Sebastiaan Mulders, Paul Fleming, David Schlipf, Kathryn Johnson, Lucy Y. Pao

Abstract:
Controls research plays an important role in wind energy.  Advances in controls are making wind turbines more efficient, more reliable, and more cost-effective. Wind turbines have evolved from passively controlled machines to actively controlled machines, and more recently, to distributed machines controlled collectively (wind farms).  With this open session, we invite researchers to present their latest results in wind energy control. The attendees of these sessions will learn how controls research can make substantial contributions to wind energy, and they will also get an overview of the latest developments and open issues. Example contributions include: ‘smart’ rotor control, lidar-based control, control of floating turbines, and wind farm control.

Web site: none
Code for submitting contributions: 1w49h
Full description: PDF

Emerging Challenges and Directions of Advanced Battery Management

Track proposed by:
Gregory L. Plett, Scott Trimboli, Simona Onori, Huazhen Fang

Abstract:
Recent years have witnessed rapid advances in the research and development of control-theorydriven advanced battery-management systems. Despite significant progresses in this field, new problems and challenges continue to arise, posing a pressing need for more research efforts to keep pace. Focused on the emerging problems and directions in this field, this open invited track is meant to provide a timely forum for researchers from both academia and industry to demonstrate state-ofthe-art results and share visions about future explorations. The discussions will not only offer strong insights, inspiration, and incentives to the audience, but also hopefully translate into a lasting impact on future battery management research. 

Web site: none
Code for submitting contributions: uxctn
Full description: PDF

Marine Robotics: the Breeze of Innovation and Remote Access to the Sea

Track proposed by:
Marco Bibuli, Enrica Zereik

Abstract:
Autonomous marine systems represent an extremely attractive research field that poses formidable challenges both from a theoretical and a practical standpoint. Many core problems in these areas are still open, and considerable research work is required to address and solve them. The complexity of the problems at hand requires a multifaceted approach to system analysis and design to exploit the use of methods and tools from dynamical systems theory, automatic control, networked systems, identification and estimation, computer vision, communications, sensing and measurements to yield practical systems capable of executing complex scientific and extended missions at sea in an efficient and reliable manner. For these reasons, there is a strong interest in bringing together the marine robotics community with specialists of complementary areas to foster new synergies and promote joint research activities aimed at solving both theoretical and practical problems with far reaching implications on scientific, commercial, and societal marine-related issues. It is against this backdrop of ideas that we submit an invited open track proposal entitled Marine Robotics: the Breeze of Innovation and Remote Access to the Sea, aimed at bringing attention to this exciting field of research and promoting the cross fertilization of ideas required to bring new theoretical and practical advances to bear on the development of innovative systems.

Web site: none
Code for submitting contributions: w3751 
Full description: PDF

Advances in control, communication, and optimization for smart charging and vehicle-to-everything (V2X)

Track proposed by:
Yang Li, Chih Feng Lee, Francesco Liberati, Volkan Kumtepeli, Daniel Quevedo

Abstract:
Electric vehicles (EVs) are increasing significantly due to their advantageous characteristics, including higher energy efficiency, lower environmental impacts, and better economic performance. Smartly managing and controlling the charging patterns of EVs is considered a crucial step for the large adoption of EVs, while the limitation on battery degradation and efficiency and ambient conditions must be well addressed. In addition, the integration of EVs and electrical grids is important, not only in terms of charging management for reducing the grid impact but also for providing an opportunity for EVs to have active participation to support the grid through vehicle-to-home (V2H) and vehicle-to-grid (V2G) technologies. This track focuses on several aspects related to charging control strategies and vehicle-to-everything (V2X) functions and covers a broad range of technology, regulation, standards, demonstration, and social influences.

Web site: https://sites.google.com/view/ifac2023-v2x/
Code for submitting contributions: ai95i
Full description: PDF

Challenges and Future Directions of Autonomous Driving

Track proposed by:
Marcel Aguirre Mehlhorn, Yuri A.W. Shardt

Abstract:
Recently, the trend of automated driving systems (ADS) is a growing application field that offers the potential for a safe and efficient future of mobility. Nonetheless, based on the SAE J3016 Level 3 or higher level, ADS must handle changing traffic conditions during operation. A variety of subsystems have to sense and comprehend the environment while correctly predicting the behaviour of other road users. Hazardous situations must consistently be recognised to choose the safest trajectory. In addition, the ADS has to be attentive to enter a safe state in case of leaving the operating limits. For this reason, combining all subsystems into a unified and functioning self-driving system is a significant challenge, providing the opportunity to continuously improve an ADS’s capabilities. At the same time, the safety of the occupants and compliance with standards and road regulations must always be achieved. This invited session will allow academic and industrial researchers to exchange their ideas and thoughts to find common problems and solutions for developing and safeguarding ADS.

Web site: none
Code for submitting contributions: uevan
Full description: PDF

Connected and Autonomous Vehicle Applications: Estimation Perspectives

Track proposed by:
Ali Zemouche, Zehor Belkhatir, Rajesh Rajamani

Abstract:
The objective of this open invited track proposal consists in inviting contributions on the role of estimation in connected and autonomous vehicle (CAV) applications. The aim is to bring together experts on estimation theory, CAV researchers, and experts of deep-learning, computer vision, and artificial intelligence, to conduct discussions on the recent advances and identify novel research directions in this multidisciplinary field. This open invited track could be the source of inspiration for innovative work from the fusion of classical control theory, computer vision and artificial intelligence.

Web site: none
Code for submitting contributions: 27m51
Full description: PDF

Machine Learning in Automotive Powertrains

Track proposed by:
Frank Willems, Mahdi Shahbakhti

Abstract:
Machine Learning (ML) is generally considered to be a disruptive technology. ML-based methods have received growing interest due to the increasing availability of data and the success of ML applications for complex problems. In the automotive sector, various studies can be found on applications in computer vision, autonomous driving or logistics and traffic planning. For powertrain applications, ML is believed to dramatically reduce the development time and costs and to enhance robust performance by self-learned adaptation. However, only limited studies are found so far. This open invited track aims to address the potential and challenges of ML-based concepts for automotive powertrains. It will create an inspiring discussion platform to bring together experts from relevant disciplines and helps to create new collaborations and to direct future research.

Web site: none
Code for submitting contributions: 6w138
Full description: PDF

Automotive Control

Track proposed by:
Per Tunestal, Carlos Guardiola

Abstract:
Despite a history of several decades automotive control is a rapidly evolving discipline. Advances in computational, mechanical and communication technologies have enabled control strategies for powertrains as well and vehicles that would be considered science fiction only a decade ago. This open invited track aims to address new research in automotive control relating to vehicle security and safety, traditional powertrain systems, electrified powertrain systems and smart mobility. It will create an inspiring discussion platform to bring together experts from relevant disciplines and help to create new collaborations and to direct future research.  
This open invited track solicits submissions of IFAC-regular papers or discussion papers (i.e. 2-4 page extended abstracts) based on original research. The focus is on vehicle security and safety, traditional powertrain systems, electrified powertrain systems and smart mobility. The topics of interest include, but are not limited to vehicle security and safety, traditional powertrain systems, powertrain modeling and control, electrical powertrain systems, and smart mobility.

Web site: none
Code for submitting contributions: v74fy
Full description: PDF

Connected and Autonomous Vehicles: Scenarios & Applications

Track proposed by:
Swaroop Darbha, Tielong Shen

Abstract:
Connected and Autonomous vehicles (CAVs) are envisioned to network with their peers, infrastructure, and pedestrians to enhance safety and mobility. Recent interest in Urban Air Mobility brings about a wide variety of practical challenges for cargo drones (CAVs) in modeling, control, collision avoidance and traffic management. The aim of this open invited track is to provide a forum for researchers in academia, scientific research laboratories, and industry to share and discuss the state-of-the-art in theory and practice of CAVs. 

Web site: none
Code for submitting contributions: irc58
Full description: PDF

Modelling, optimization and control for sustainability

Track proposed by:
Michela Robba, Marialuisa Volta, Riccardo Minciardi, Roberto Sacile, Federico delfino, Francois Peres, Giorgio Guariso, Luca Ferraris

Abstract:
In the last years, several negative environmental impacts have affected our world (climate change, pollution of water, air soil, resources scarcity, waste increase, etc.) that created the necessity of defining new alliances and regulation at international level to protect our environment. The Sustainable Development Goals are the blueprint to achieve a better and more sustainable future for all. They address the global challenges we face, including those related to poverty, inequality, climate change, environmental degradation, peace and justice. Within the goals of Sustainable Development, this Open Invited Track is focused on the impact on environmental systems, including three main macro-areas: pollution (water, air, soil); natural and technological risk management (floods, forest fires, dangerous goods transportation and production); conservation of resources (circular economy, waste management, sustainable energy production and distribution, etc.). In particular, attention is posed on models, methods and technologies that are useful to improve the management of the above mentioned areas of research. The proposed Open Invited Track aims to collect new advances in the study of sustainability, both from an application and methodological point of view. 

Web site: none
Code for submitting contributions: 7hv7x
Full description: PDF

Water asset management based on innovative Control approaches

Track proposed by:
Eric Duviella, Laurent Lefevre, Jose M. Maestre, Carlos Ocampo-Martinez

Abstract:
The summer of 2022 was particularly revealing in terms of climate change, with a strong and lasting drought in Europe and record rainfall in Asia. These events further demonstrate the high vulnerability of all human activities to the availability of water resources. Although the Automatic Control community has contributed to the improvement of water resource management for several decades, climate change and the increase in the world's population show that new challenges still lie ahead. It is requiring the development and design of i) models of hydro-systems based on a combination of physical and digital solutions, ii) tools for water management dealing with multi-risk and multi-constraint approach, with large-scale and uncertainties, iii) implementation of management strategies for adaptation and mitigation to extreme events; drought and flood. The invited tracks in the two last edition of the IFAW WC demonstrate the great interest of researchers for this topic with contributions in modelling (data-driven model, predictive model, model dealing with uncertainties) and control algorithms (Model Predictive Control, optimal control, among many others). Organizers are waiting contributions from the Automatic Control community gathering multidisciplinary expertise from researchers belonging to Computer Sciences (Machine Learning, Artificial Intelligence) and Control Theory. This track offers the opportunity to exchange ideas and interact between researchers. 

Web site: none
Code for submitting contributions: 31jx4
Full description:PDF

Digital twins to improve medical care

Track proposed by:
Thomas Desaive, J. Geoffrey Chase, Yeong Shiong Chiew, Fatanah Suhaimi, Jennifer L. Knopp, Levente Kovacs, Cong Zhou, Clara Ionescu

Abstract:
Aging populations and chronic diseases (diabetes, COPD, …) have increased stress on medical resources with demand starting to exceed availability ? a problem in need of the right technologies to improve care and productivity. As computational, control and sensor technologies advance, the potential in application to medical and biological systems has increased exponentially. As a result, there has been an increasingly tight inter-relation between engineering and clinical medicine. Digital technologies and automation have improved productivity in many industrial fields but few in medicine. The concept of digital twins was built at the intersection of industry 4.0 and the Internet of Things, based on the development of big data, sensors and cloud computing. The digital twin is a virtual copy of a system capable of interacting bidirectionally and in real-time with the physical system. This session will focus more specifically on digital twins in medicine, namely the direct use of individual-specific computer models for the prevention, prediction, screening, diagnosis and treatment of a disease, as well as the evaluation, optimization, selection and personalization of intervention options. The digital twins are built using the knowledge coming from biology, physiology and medicine.  In addition, they can be augmented with clinical data. Digital twins provide a patient-specific means to answer to therapeutic questions, from the discovery of new therapies to the prediction of the effect of a proposed therapeutic intervention and optimization of care.

Web site: none
Code for submitting contributions: ua8y1
Full description: PDF

Control, Mechatronics, and Imaging for Medical Devices and Systems in Medicine

Track proposed by:
Thomas Desaive, J. Geoffrey Chase, Thomas Schauer, Marcos de Sales Guerra Tsuzuki, Balazs Benyo, Knut Moeller, Christopher Pretty, Yeong Shiong Chiew

Abstract:
There is growing convergence of technology and demographics as they impact our ability to provide healthcare. Aging populations have increased stress on medical resources with demand starting to exceed availability – a problem in need of the right technologies to improve care and productivity. 
As computational, control and sensor technologies advance the potential in application to medical and biological systems has increased exponentially. As a result, there has been an increasingly tight inter-relation between engineering and clinical medicine. This session focuses on the novel hardware/software design and control of medical devices to impact care.
This session focuses, on novel design and application of medical devices and systems, including any related systems modeling, control and system dentification, emphasising the novelty in the hardware/software (and their integration) of medical devices.

Web site: none
Code for submitting contributions: p35m3
Full description: PDF

Plant Factory and Urban Agriculture

Track proposed by:
Manoj Karkee, Jayantha Katupitiya, Timo Oksanen, Minzan Li, Shih-Fang Chen, Selwin Hageraats, Satoru Sakai

Abstract:
Two decades into the 21st century, the world is facing continued population growth and an increasing impact of climate change. This has led to rapidly expanding population centers and a rise in extreme weather events and long-lasting droughts. In order to keep the world population fed in a reliable manner, many parts of the world can no longer rely solely on open-field agricultural production. By making use of greenhouses and vertical farms, higher yields can be realized on smaller areas, while protecting crops from excessive heat, cold, or precipitation, consuming (much) less water, and radically reducing transportation miles from farm to consumer. Moreover, the closed environments in greenhouses and vertical farms allow for more control over cultivation parameters, enabling further optimization of crop yield and paving the road towards autonomous cultivation. This Special Session welcomes the contribution from researchers, engineers, and industrial practitioner to present the latest advances including but not limited to: 
- efficient quality food production  
- sensor network 
- mechatronic automation 
- optimization and modeling 
- decision support system 
- circular agriculture

Web site: none
Code for submitting contributions: dgx9g
Full description: PDF

New Trends in Control and Optimization in Smart City Networks

Track proposed by:
Michela Robba, Giulio Ferro, Rong Su, Anuradha Annaswamy, Christos G. Cassandras, Karl H. Johansson, Masayuki Fujita, Toru Namerikawa

Abstract:
In recent years, there has been a growing interest in sustainable and smart cities. Increasingly, cities need more efficient water, transportation, and energy systems to address various challenges, including a growing population, environmental and economic sustainability, and resiliency to natural disasters and unpredictable events. Advanced technologies for data collection, information processing, and decision-making are being developed, accompanied by advances in technologies for mitigating greenhouse gas emissions reduction such as renewables, electric vehicles, space heating, and industrial processes; all together these form the foundation for smart city networks and provide the focus for this special issue.

Typical examples of smart city networks are electrical distribution grids characterized by different nodes and/or clusters of microgrids, and transportation networks in which links represent roads and nodes represent crossings. Often, these networks are interconnected and interacting, examples of which are charging stations for electric vehicles which couple power and transportation networks. District heating and electricity networks are often coupled, as are water distribution and district heating. In most of these networks, a common theme is an increasing shift towards solutions consisting of automation, digitalization, networking through the Internet of Things (IoT), thereby underscoring the need for developing tools for collecting, monitoring, and processing large amounts of data, analyzing and synthesizing real-time control algorithms, and carrying out studies that are scalable and are capable of handling emergencies. The proposed Open Invited Track aims to collect new advances in the study of smart city networks, both from an application and methodological point of view.

Web site: none
Code for submitting contributions: q1d9b 
Full description: PDF

Young graduate researchers in Asian institutes

Track proposed by:
Daisuke Kurabayashi, Noboru Sakamoto, Yoshio Ebihara

Abstract:
This Open Invited Track (OIT) aims at promoting interactions among IFAC WC participants and young researchers in Asia working in robotics and control systems fields. Since robotic technologies are highly demanded in our society, there are many novel challenges, which have not been achieved with solid theoretical proof. Control systems research also faces significant challenges prompted by the recent progress in network science, machine learning, and big data science. This OIT welcomes not only accomplished research results but also those in early stages, including proposals of innovative mechanical structures, system integrations, control theory, and valuable applications in robotics and control systems, or other topics related to robotics and control systems technologies. This OIT plans to bring many young people together who have not yet joined any events of IFAC. For this purpose, the presentation type of this OIT allows only interactive (poster) sessions and accepts only discussion papers (in an extended abstract form between 2 to 4 pages). Submitted papers will be evaluated in Technical Committee 9.4 (Control Education).

Web site: none
Code for submitting contributions: 24c46
Full description: PDF

Control for Socio-Technical Network Systems

Track proposed by:
Giacomo Como, Paolo Frasca, Francesca Parise, Ketan Savla

Abstract:
The confluence of methods from control, game theory, and network science is increasingly driven by applications to large-scale complex socio-technical systems. Socio-technical systems are defined by the co-presence and interdependence of technological and human/social elements. Socio-technical systems appear in diverse domains of society and technology. Chief examples are transportation systems, in which the physics of traffic interplays with the collective choices of the users (drivers, pedestrians, commuters), as well as social media, in which users interact through online platforms (such as Facebook or YouTube) that act as information gate-keepers. Overall, these systems require specific tools to capture their salient features, including their intrinsic heterogeneity, the large size of the users’ pool, the increasing role of Artificial Intelligence in their operation, the role of human decision making and the variety of spatial and temporal scales that are involved in their dynamics. This session aims to establish a forum to discuss both general methodologies and domain-oriented contributions, including collaborations with domain experts (social scientists, psychologists, transportation experts, and so on).

Web site: none
Code for submitting contributions: 5xf1h
Full description: PDF

Human Machine Symbiosis: Perspectives on emerging digital trends and their social impact

Track proposed by:
John Organ, Mary Doyle-Kent, Larry Stapleton, Peter Kopacek

Abstract:
This invited track session welcomes contributions to interdisciplinary research at the convergence point of emerging systems and the social sciences concerned with the development of new technologies that valorise the human. This track addresses challenges and opportunities associated with engaging the human communities in the development of complex socio-technical semi-automated systems. The emerging waves of digital technologies continue to impact and disrupt communities in ways heretofore never seen, offering both wonderful potential and perilous danger to the human condition and our shared global society.  
Technology developments leads to dramatic changes in international stability. global systems of information, commerce, politics, finance, and natural resources have had both negative and positive consequences for international stability. This track welcomes contributions from multidisciplinary academic and practice-based communities that attempt to understand these problems from a human centred systems engineering perspective. We seek to understand and address social effects associated with the proliferation of automation and control systems and strategies that improve health and education, reduce inequality, and encourage economic growth whilst tackling emerging threats linked to the UN Sustainable Development Goals.  
Topics of the proposed Open Invited Track are specifically related (but not limited to) the following application areas:  
Human-centred systems, Artificial Intelligence and Applications, Control & Political Stability, Cross-cultural Aspects of Engineering, Education for TECIS, Engineering Ethics, Diversity and Inclusion, Innovation Management Intelligent Systems and Applications, Social Networks, Sustainable Design and Control Technology in Post-conflict Regions, New technologies for the environment, Tele-medical Systems, Innovation management, Young Engineers in Control 

Web site: none
Code for submitting contributions: p9875
Full description: PDF

Social Innovations Improving Sustainability of Social Systems and Silver Economy in Smart Cities and Communities

Track proposed by:
David Bogataj, Alenka Temeljotov Salaj

Abstract:
Older adults are the fastest-growing demographic group in Europe, influencing the demand for health care and social care services. The functional capacities of older adults are decreasing and require better adaptation to living and working environments. Governments and the private sector should develop new policies, technologies, management and control of processes to satisfy the needs of the ageing population with declining functional capacities. Social innovations can be defined as the development and implementation of new ideas (products, services and models) to meet social needs and create new social relationships or collaborations that promote new responses to pressing social demands, which affect the process of social interactions and are aimed at improving human well-being. Therefore, many new innovative ICT-based products, services, and systems that are providing support to older people in living an independent and autonomous life, such as e-Health, m-Health, e-Care, m-Care, Ambient Assisted Living (AAL) and Ambient Intelligence technologies, are under development and are tested in pilot projects and living labs. This technology supports the development of social infrastructure in Smart Cities and Communities of ageing regions and the age management in the ageing society and is an important part of the emerging Silver Economy. 

Web site: none
Code for submitting contributions: 5gy29
Full description: PDF