Pre-Conference Workshops

The 62nd IEEE Conference on Decision and Control (CDC 2023) will be hosted at Marina Bay Sands, Singapore, from December 13 through December 15, 2023. Pre-conference workshops will take place on December 12 at the same venue.

Workshops

1. Semi-Tensor Product of Matrices and Its Applications

Abstract: In the past decade the semi-tensor product (STP) of matrices has been the subject of extensive research and the theory of STP has been successfully applied to modelling and control of Boolean (control) networks (BNs, BCNs), evolutionary games, cross-dimensional control networks, just to cite a few. This workshop aims to provide a tutorial introduction to STP and its applications. The BN, introduced by Stuart Kauffman in 1969, is an effective model for gene regulatory networks, as well as some other networks. The use of STP to derive the algebraic representation of BNs and BCNs will first be illustrated. Then several recent developments about control problems BCNs will be presented, including reconstruction, optimal control, observer design, fault detection, control of probabilistic BCNs, observability, synthesis based on state-feedback control, etc. The application of STP to finite games, such as potential games, networked games, etc., will also be discussed. STP-based mix-dimensional Euclidian spaces and dimension-varying dynamical systems over such spaces will be discussed. Applications of STP to engineering problems, such as mixed energy vehicles and power systems, will be presented.

2. Control, Game, and Learning Theory for Security and Privacy

Abstract: In today's increasingly connected world, cybersecurity has emerged as a major challenge due to the ubiquitous digitalization affecting every aspect of society, life, and work. Traditional approaches to network security, such as cryptography, firewalls, and intrusion detection systems, are no longer sufficient to guarantee the security of the network as attackers become more sophisticated. Therefore, there is an urgent need to shift to a new security paradigm that takes into account the strategic behaviors and constraints on attack-and-defense resources. The workshop aims to create a platform for the discussion of the theoretical foundations of security games. It provides a forum to discuss new modeling frameworks, analytical methods, and algorithmic solutions that bridge cognitive science, decision and control theory, data science, and network science to solidify the foundations of security games. This workshop will be supported by the IEEE CSS Technical Committee on Security and Privacy to reach out to members of the control systems community and other research communities, including communications, machine learning, and computer scientists. It is crucial to bring together experts from different communities and foster discussions to create a community and overcome the fragmentation of previous work. Through this workshop, experts aim to pave the way for more robust and effective security solutions in the future.

3. Distributed Control, Optimization and Learning for Multi-agent Systems

Abstract: Rapid developments in digital systems, communication technologies, and sensing devices have led to the emergence of large-scale networked systems connecting a massive number of intelligent agents. Motivated by applications such as control, decision-making, machine learning, and signal processing in these networked systems, the agents are often required to jointly solve control, optimization and learning problems so that a desirable intelligent system can operate effectively in complex and dynamic environments will be achieved. Due to the distributed nature of the networked systems, the traditional centralized strategies are not suitable to address those optimization problems, as they suffer from performance limitations such as vulnerability to single-point failures, costly communications and computations, and lack of flexibility and scalability. This motivates the development of distributed control, optimization and learning algorithms for multi-agent systems. The objective of this workshop is to provide a platform for researchers to exchange ideas and share recent developments in distributed control, optimization, and learning for multi-agent systems. The workshop will feature invited talks from leading researchers in the field. We believe that this workshop will provide an excellent opportunity for researchers and practitioners to exchange ideas, and advance the state-of-the-art in multi-agent systems.

4. Population Games: Strategic Multi-Agent Interactions at Scale

Abstract: For a complex system consisting of many agents interacting strategically with one another, key research themes are to understand how individual agents’ decision-making influences the emergent behavior of the system and analyze the system’s long-term behavior. To model the dynamics of decision-making in response to payoff mechanisms, researchers have turned to population game frameworks in recent decades. These frameworks have been employed in applications as diverse as transportation networks, wireless networks, smart grids, and cloud computing. It is most likely that the full potential of considering more sophisticated agent decision-making models, dynamic payoff mechanisms, and disspativity-based techniques in engineering applications has not yet been exploited because the key concepts and results needed for such work have been originally published in disparate venues that pose a steep language, stylistic, and conceptual barrier to their assimilation by the control systems community. The proposed workshop intends to bridge this gap, while also putting forward new dissipativity-based techniques for verification and design in population games and their applications in engineering fields.

5. Modern Adaptive Control and Estimation: From Theory to Applications

Abstract: As a major methodology for handling parametric uncertainties in dynamical systems, adaptive control/estimation has attracted much attention in both academia and industry over the past few decades. The classical adaptive control/estimation imposes appropriate structural knowledge on parametric uncertainties and achieves only asymptotic error convergence with weak robustness in the absence of a stringent condition termed persistent excitation, which prevents it from widespread applications in real-world systems. In recent years, some advanced adaptive design concepts have been proposed to overcome the above limitations, where notable ones with great potential in practice include regressor extension, online optimization, and non-Euclidean adaptation. These approaches have resulted in several successful real-world applications, but they are limited to relatively simple systems with low degrees of freedom, and in-depth considerations about application issues are rare. This workshop aims to bring together researchers and practitioners from academia and industry in a forum, which will help us bridge the gap between advanced theory and its real-world applications. Our objective is to create an inclusive environment where all participants feel welcomed and valued and where a diversity of ideas and approaches can be shared and discussed. This diversity will enrich the workshop experience and contribute to the overall success of the conference.

6. Counter-adversarial inference, control and learning: New Frontiers, Newer Challenges

Abstract: Counter-adversarial inference, control and learning theory has received significant traction over the past few years. However, this area is far from being mature. Technological advancement is increasingly catering to the complexity and scalability of these problems and solution techniques, and the changing nature of the interaction among agents in new technological domains are giving rise to more challenging problems. This is the motivation behind the proposed workshop-eminent researchers from diverse backgrounds will meet, present their work and discuss future research directions. While the workshop will not exhaustively cover all emerging research directions in this domain, it exhibits its uniqueness in choosing the technical topics spanning a large new spectrum including robust control and learning, decision making against adversaries, multi-agent control over networks, consensus, security, trust, control for MIMO communication (contrary to popularly studied problems on control over communication network) and mean field games between teams. Despite being such a vibrant field, there has been no special issue from the IEEE control society on counter-adversarial inference, control and learning in recent times, though papers on some of these topics are published in control and learning theory venues in isolated manner. A special session is not sufficient to cover so many topics, and hence a workshop will be an ideal venue for dissemination of knowledge in this field.

7. Physics-informed Learning for Control and Optimization

Abstract: While machine learning techniques have shown remarkable success in various domains, their application to control has often been hindered by their inherent limitation: a lack of consideration for the underlying physical laws and constraints that govern the behavior of any real-world dynamical system. As a result, the models often lack in trustworthiness and generalizability. However, with the emergence of physics-informed machine learning, a new paradigm is taking shape - one that combines the power of data-driven learning with the foundational principles of physics. This workshop aims to provide insight into recent advances in the field of physics-informed machine learning for control and optimization, and sketch some of the open challenges and opportunities using physics-informed machine learning. Experts/lecturers with experience in physics-informed learning and optimization-based control will present new results in this area and spotlight challenges and opportunities for the control community as well as recent advances in physics-informed learning in general. The workshop targets an audience from graduate level to experienced theoretical and practically oriented control engineers who aim to improve their knowledge in physics-informed machine learning for control and optimization.

8. Formal Methods and Decision Making in the Age of AI

Abstract: The rapid advancement of machine learning and AI is leading to a paradigm shift in the way we make high-level decisions and low-level control for autonomous and robotic systems. While these advancements present exciting opportunities towards building intelligent systems, it also introduces new challenges, such as dealing with the fragility of neural networks, that require novel solutions. Our workshop on “Formal Methods and Decision Making in the Age of AI” aims to unravel these challenges and open problems for a general audience and discuss what new principles and techniques we need for perception-enabled system design, scalable design of distributed systems, verifiable learning-enabled systems, systems with humans in the loop, and safety in autonomy. The workshop will provide a platform for theoreticians as well as practitioners from the fields of systems & control theory, formal methods, machine learning & AI, and applied mathematics to come together and discuss their latest research and emerging trends. Participants will be able to share their insights with each other on the current state-of-the-art in formal methods, decision making and AI, and explore opportunities for interdisciplinary collaborations. Overall, the workshop aims to advance our understanding of the challenges and opportunities that arise from the use of AI in decision making. By bringing together experts from different fields, the workshop will facilitate interdisciplinary collaborations and foster the development of new approaches that can help ensure the safe and effective use of AI technologies.

9. Workshop on Benchmarking, Reproducibility, and Open-Source Code in Controls (Call for Short Abstract Submissions)

Abstract: Over the past years, the scientific community has grown more cognizant of the importance and challenges of transparent and reproducible research. This topic has become increasingly important given the rise of complex algorithms (e.g., machine learning models or optimization-based algorithms), which cannot be adequately documented in standard publications alone. Benchmarking and code sharing are two key instruments that researchers use to improve reproducibility. Benchmarks have played a critical role in advancing the state of the art in machine learning research. Analogously, well-established benchmarks in controls could enable researchers to compare the effectiveness of different control algorithms. There are currently only a few benchmarks available for comparing control algorithms (e.g., the Autonomie simulation model of a Toyota Prius or the shared experimental testbed Robotarium). Limited comparisons are also due to the modest number of open-source implementations of control algorithms. Over a six-year period (2016-2021), we found that the percentage of papers with code at CDC has more than doubled. However, we also found that at CDC 2021 only 2.6% of publications had code (compared to around 5% at the robotics conference ICRA and over 60% at the machine learning conference NeurIPS). These trends are encouraging, but there is still much work to be done to promote and increase efforts toward reproducible research that accelerates innovation. Benchmarking and releasing code alongside papers can serve as a critical first step in this direction. Our workshop aims to increase awareness of these challenges and inspire attendees to contribute to benchmarking efforts and share open-source code through publication in the future.

10. Learning Enabled Control and Coordination for Societally-Aware Transportation Systems

Abstract: In recent years, there have been significant advancements in using data-driven techniques to control and coordinate large-scale transportation systems. Typically, these techniques are designed to maximize the efficiency of the system, by minimizing delays and transportation costs. Future transportation systems however should ideally be designed not only to maximize efficiency but also to address societal objectives such as fairness, robustness, privacy, and sustainability. These socially-oriented desiderata introduce several challenges, including (i) requiring the consensus and coordination among agents on acceptable definitions of and tradeoffs between these desiderata, (ii) the computational intractability of traditional approaches, and (iii) the consideration of data-availability issues due to privacy concerns. The objectives of the workshop are four-fold: (a) Emphasize data-driven machine learning techniques in the context of transportation applications. The discussion of data-driven modeling, analysis, and control tools in applications such as road traffic management will help foster methodological developments to design societally aware algorithmic decision-making systems for emerging mobility systems; (b) Provide a platform to explore new research directions geared towards tapping the full potential of data in achieving societal objectives in transportation systems; (c) Foster the development of novel control mechanisms essential for advancing societally aware transportation systems and enable discussions on the appropriate measures for societal considerations such as fairness, equity, robustness, privacy, etc.; and (d) Introduce the controls community to transportation-specific models for societal objectives, highlight limitations of the current models, and challenges faced in implementing these objectives using traditional approaches.

11. Systems Theory of Ensembles: Fundamentals, Learning, and Applications

Abstract: The emergence of complex systems constituted by a vast ensemble (population) of structurally similar dynamical units (agents) has created massive waves driving the recent research in systems science toward learning, engineering, and controlling the collective dynamics and behavior of population systems. Notable examples appearing across disciplines include excitation of spin ensembles in applications of nuclear magnetic resonance, effective stimulation of neuronal populations in treatment of neurological disorders such as Parkinson’s disease, decoding and inference of dynamic topology and functional connectivity in dynamic networks, development of autonomous intelligent machines or factories in interconnected spatiotemporally dynamic cyber-physical systems, as well as the mediation of epidemic outbreaks witnessed in recent years. In this workshop, we will offer a comprehensive introduction into the recently spurred, highly exciting and rich field of ensemble control that inspires open challenges and new opportunities for control theory concerning with high-dimensional and very large-scale phenomena. Emphasis will be placed on surveying the fundamental theoretical results of this area in the beginning, and then conveying both state-of-the-art methods for theoretical, computational, and data-driven treatments and emerging applications at the forefront and interface of systems science, control engineering, data science, machine learning, quantum physics, neuroscience, and biology.

12. Learning and control for decarbonized energy and transportation systems

Abstract: Climate change is the most pressing problem facing humanity in the coming decades. To address this challenge, there are significant efforts underway at the international level towards decarbonization of our critical societal infrastructures. A critical pathway in this regard is joint decarbonization of the electricity and transportation sectors, which are the two largest contributors to emissions worldwide (at nearly 25% and 29% of total GHG emissions, respectively.) The Control for Societal-scale Challenges: Road Map 2030 from the IEEE Control Systems Society also identifies climate change and resilient infrastructures as key areas that require research advances led by the control community. From an engineering perspective, accomplishing decarbonized energy and transportation requires large-scale integration of electrified mobility, renewables, distributed energy resources (DERs), storage, and alternative fuels like hydrogen. However, this transition poses serious operational challenges in terms of grid stability and resilience due to uncertain loads like electric vehicles being served by volatile sources like renewable generation. These reliability challenges are only expected to be further compounded due to climate change induced extreme events. Thus, safe, optimal and reliable operation of decarbonized energy-transportation infrastructures will require advances at the intersection of control, optimization, and machine learning at every stage.

13. Autonomous Unmanned Systems Technologies and Applications

Abstract: In recent decades, the academia and industry have paid more and more attention to and investment in the research and development of autonomous unmanned systems. Autonomous unmanned vehicle is a machine equipped with necessary data processing units, sophisticated sensors, environment perception, automatic control, motion planning, task planning and mission management, as well as communication systems. It can perform and complete certain specific tasks autonomously without a human operator. It is an integration of advanced technologies in many fields including deep learning for perception, learning based control and navigation, and multi-agent system technology. Autonomous systems, such as unmanned ground vehicles (UGV), unmanned aerial systems (UAS), unmanned surface vehicles (USV) and unmanned underwater vehicles (UUV), are projected to play significant roles in industrial applications, such as reconnaissance for search and rescue, security surveillance, environmental and traffic monitoring, powerline and pipeline inspection, building inspection, geographic mapping, tunnel inspection, film production, logistic delivery, and warehouse management. The proposed workshop on autonomous unmanned systems technologies and applications aims to provide audience with up-to-date information and latest technologies involved in developing autonomous unmanned aerial systems and in tackling some real industrial problems, such as infrastructure inspection and information management. The workshop will also contain presentations given by teams participating in Cooperative Aerial Robots Inspection Challenge on their solution and results.

14. Control Barrier Functions: Recent developments and future directions

Abstract: The modern formulation of control barrier functions was introduced about a decade ago with the goal of providing a computationally efficient way of ensuring safety of dynamical systems, intended as the controlled invariance of a subset of the system state space. Since then, we have witnessed a tremendous amount of developments related to several aspects of controllers defined leveraging control barrier functions and optimization-based methods. These aspects include the feasibility, robustness, adaptivity of the approach, but also the extensions of safety-critical controllers to many different domains, such as robotics, aerospace, but also economics and epidemiology. This workshop aims at summarizing recent findings related to control barrier functions and providing a venue for the control community to discuss relevant and promising future research directions to pursue. This objective will be achieved through a proposed workshop program that features (i) invited talks given by experts in the field of control barrier functions, (ii) an interactive poster session during which workshop attendees will have the possibility of showing their latest findings, and (iii) a panel session where future research directions will be highlighted and discussed involving both selected panelists and the workshop attendees.

15. Formal Methods in System Resilience: From Analysis to Control

Abstract: Engineering systems that involve physical elements controlled by computational infrastructure are called Cyber-Physical Systems (CPS). CPS are present in almost every modern automated system, ranging from manufacturing and transportation systems over telecommunication networks to large-scale computer clusters. The ever-increasing demand for safety, security, performance, and certification of these – often safety-critical – CPS put stringent constraints on their design. This necessitates the use of formal, model-based approaches to analyze and design secure, reliable and performant systems. This workshop aims to report recent research achievements related to formal analysis and control for resilience and to identify relevant challenges. It will focus on two main themes: (i) Formal Analysis for Resilience, which include safety verification, diagnosability/detectability analysis of DES in networked environments under attacks, information-flow security analysis and efficient resilience verification for infinite systems; and (ii) Formal Control Synthesis for Resilience, which include supervisory control theory of DES under attacks, resilient software synthesis by reactive synthesis and secure-by-construction synthesis of cyber-physical systems.