Workshops

CDC 2023 is committed to being not only the premier venue for the dissemination of work in automatic control but to also provide an environment that is inclusive and supportive to our members. We are aiming to make CDC 2023 a model for other IEEE Societies in diversity, equity, and inclusion, and to create a forum where we broadly reach out to elicit the best scholarship, innovation, and breadth of ideas that our community has to offer. To help us achieve our goals, workshop proposals should include a statement explaining the diversity of the session along dimensions such as gender, geography, industry/academia affiliation and other forms of diversity. We welcome any additional forms of diversity you wish to articulate.

The workshop co-chairs invite your submission of a workshop proposal. A proposal should focus on a specific theme related to the main conference topics, and include:

(1) objectives,
(2) expected outcomes,
(3) expected attendance,
(4) the list of speakers along with their short bio,
(5) a statement explaining the diversity of the session in terms of gender, geography, industry/academia affiliation and other forms of diversity, and
(6) the schedule for a full day workshop (proposals for half-day workshops will not be accepted).

Proposals should be submitted through PaperPlaza. The deadline for the submission is on May 15, 2023.

Organizers: Daizhan Cheng, Maria Elena Valcher, Kuize Zhang

Location: Peony Junior 4512

Website: https://m-stp.lcu.edu.cn/xzhdyg/520022.htm

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.

Lecture Schedules: 

• 9:00 - 9:30 An Introduction to STP and its applications (Speakers: M. Elena Valcher and Daizhan Cheng)
• 9:30 - 10:15 Observer design and fault detection of Boolean control networks (Speaker: Ping Zhang)
• 10:15 - 10:45 Coffee Break
• 10:45 - 11:30 Observability verification and synthesis based on state-feedback control in Boolean control networks (Speaker: Kuizhe Zhang)
• 11: 30 - 12:15 Reconstruction of Boolean networks and optimal control (Speaker: M. Elena Valcher)
• 12:15 - 14:00 Lunch Time
• 14:00 - 14:30 The control problem of probabilistic Boolean control networks (Speaker: Carmen Del Vecchio)
• 14:30 - 15:00 Application of STP to finite games (Speaker: Jiandong Zhu)
• 15:00 - 15:45 Mix-dimensional Euclidian space and cross-dimensional (control) systems (Speakers: Daizhan Cheng and Jun-e Feng)
• 15:45 - 16:15 Coffee Break
• 16:15 - 16:45 Practical Applications of STP-Based Logical Networks in Automotive Powertrain Control Design (Speakers: Tielong Shen and Yuhu Wu)
• 16:45 - 17:15 Demand-side management for a class of smart grid by using STP (Speakers: Xiaohua Xia and Bing Zhu)

Organizers: Tamer Basar, Quanyan Zhu

Location: Peony Junior 4511

Website: https://sites.google.com/nyu.edu/cdc2023workshop/home

Lecture Schedules: https://sites.google.com/nyu.edu/cdc2023workshop/schedule

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.

Control and game theories are mathematical sciences that study dynamical feedback systems and strategic interactions among rational decision-makers. They have emerged as promising frameworks for the analysis and design of system security. Over the past few years, control and game theories been successfully applied to various security domains, including wireless community, cloud computing, industrial control systems, Internet of Things, and national homeland security. This workshop aims to discuss the recent advances in the field and bring together experts from different communities to address the challenges of cybersecurity.

The workshop program features invited presentations that cover a diverse range of applications of game theory to security issues in cyber-physical systems, computer networks, and machine learning. A particular emphasis is placed on the intersection of machine learning and game theory for cybersecurity, an area that has garnered significant attention in recent years. The intersection enables systems to automate security solutions and adapt and learn from new data, making them better suited to address dynamic and evolving security threats. By connecting game theory, control theory, and learning theory, the workshop aims to bridge the gap between theory and application, providing a powerful set of tools that can improve the effectiveness and efficiency of security applications.

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. The topics of this workshop include:

• Game theory, control, and mechanism design for security and privacy

• Decision-making and decision theory for cybersecurity

• Security and privacy for the Internet-of-Things, cyber-physical systems, cloud computing, resilient control systems, and critical infrastructure

• Pricing, economic incentives, security investments, and cyber insurance

• Risk assessment and security risk management

• Security and privacy of wireless and mobile communications, including user location privacy

• Socio-technological and behavioral approaches to security

• Behavioral science, decision-making, heuristics, and biases

• Modeling and analysis of deception for security

• Adversarial or strategic machine learning

• AI for cybersecurity

Organizers: Tao Yang, Cesar A. Uribe, Yiguang Hong, Angelia Nedich

Location: Peony Junior 4411-4412

Website: https://neuyangtao.github.io/IEEECDC2023Workshop.html

Lecture Schedules: https://neuyangtao.github.io/IEEECDC2023Workshop.html

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.

Distributed control, optimization, and learning are essential techniques for enabling multi-agent systems to operate efficiently and robustly. Distributed control involves designing decentralized control policies that allow individual agents to make decisions based on local information while coordinating with other agents to achieve a common objective. Optimization techniques can be used to find optimal solutions for complex problems that involve multiple agents and conflicting objectives. Learning algorithms can enable agents to improve their behavior over time based on past experiences.

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.

Organizers: Shinkyu Park, Murat Arcak, Nuno C. Martins

Location: Orchid Junior 4312

Website: https://sites.google.com/view/cdc2023-population-games/home

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.

The traditional population game formalism, in which a static (memoryless) payoff mechanism influences the agents’ decisions, has recently been extended by the controls community to include dynamics in the payoff mechanism [1, 2, 3]. In the prescriptive scenario in which the payoff mechanism is engineered to be carried out by a coordinator, the dynamics may result from learning behavior, the ever-present inertia in the reward/price-setting mechanism, or the anticipative effects caused by the agents’ attempt to react to predicted future changes in rewards/prices. Allowing for dynamics in the payoff mechanism opened up immense possibilities to employ the formalism of population games to solve a wider variety of research challenges in control systems and related disciplines. As a case in point, in epidemiology, a dynamic payoff mechanism can be designed to minimize the long-term infection prevalence with an anytime bound on the peak of infections [4]. In multi-robot system applications, a payoff mechanism can be designed to coordinate multiple robots in carrying out assigned tasks in dynamically changing environments [5].

System theoretic dissipativity methods, which were originally introduced in Willems’s seminal article [6], play an important role in compositional verification and design of large-scale dynamical systems [7]. In the new formulation of population games, one can model the agents’ decision making under a dynamic payoff mechanism as a feedback interconnection of two separate dynamical system models – payoff dynamics model and evolutionary dynamics model. Consequently, dissipativity-based techniques become an essential tool in verifying stability of equilibrium states of the feedback interconnection [8]. In addition, leveraging the compositional nature of the dissipativity analysis, one can design new mechanisms underlying the games and agent decision-making models ensuring the stability in a large class of population games, despite time delays in the agent decision making [9, 10, 11].

It is most likely that the full potential of considering more sophisticated agent decision-making models, 1 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.

References

[1] S. Park, N. C. Martins, and J. S. Shamma, “From population games to payoff dynamics models: A passivity-based approach,” in 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 6584–6601.

[2] ——, “Payoff dynamics model and evolutionary dynamics model: Feedback and convergence to equilibria (arxiv:1903.02018),” arXiv.org, March 2019.

[3] M. J. Fox and J. S. Shamma, “Population games, stable games, and passivity,” Games, vol. 4, pp. 561–583, Oct. 2013.

[4] N. C. Martins, J. Cert´orio, and R. J. La, “Epidemic population games and evolutionary dynamics,” Automatica, vol. 153, p. 111016, 2023.

[5] S. Park, Y. D. Zhong, and N. E. Leonard, “Multi-robot task allocation games in dynamically changing environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 8678–8684.

[6] J. C. Willems, “Dissipative dynamical systems part I: General theory,” Arch. Ration. Mech. Anal., vol. 45, no. 5, pp. 321–351, Jan. 1972.

[7] M. Arcak, “Compositional design and verification of large-scale systems using dissipativity theory: Determining stability and performance from subsystem properties and interconnection structures,” IEEE Control Systems Magazine, vol. 42, no. 2, pp. 51–62, 2022.

[8] M. Arcak and N. C. Martins, “Dissipativity tools for convergence to Nash equilibria in population games,” IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 39–50, 2021.

[9] S. Park and N. E. Leonard, “KL divergence regularized learning model for multi-agent decision making,” in 2021 American Control Conference (ACC), 2021, pp. 4509–4514.

[10] ——, “Learning with delayed payoffs in population games using Kullback-Leibler divergence regularization (arxiv:2306.07535),” arXiv.org, June 2023.

[11] S. Park, “Tuning rate of strategy revision in population games,” in 2023 American Control Conference (ACC), 2023, pp. 423–428.

Lecture Schedules: https://sites.google.com/view/cdc2023-population-games/home#h.u52kl7gs7zj2

• 8:30 ∼ 9:15 Introduction and basic tenets of population games (Speaker: Murat Arcak)
• 9:15 ∼ 10:00 Strategy revision processes and evolutionary dynamics (Speaker: Nuno Martins)
• 10:00 ∼ 10:30 Break
• 10:30 ∼ 11:15 Dissipativity as compositional verification and design tools (Speaker: Murat Arcak)
• 11:15 ∼ 12:00 Learning with delayed payoffs in population games (Speaker: Shinkyu Park)
• 12:00 ∼ 13:30 Lunch Break
• 13:30 ∼ 14:15 Application in epidemiology: Epidemic population games (Speaker: Nuno Martins)
• 14:15 ∼ 15:00 Application in autonomous systems 1: Reshaping Urban Mobility in Traffic Networks with Mixed Vehicle Autonomy (Speaker: Negar Mehr)
• 15:00 ∼ 15:30 Break
• 15:30 ∼ 16:15 Application in autonomous systems 2: Modeling and Resolving Conflicts for Noncooperative Autonomous Systems via Markov Games (Speaker: Sarah Li)
• 16:15 ∼ 17:00 Application in autonomous systems 3: Task allocation games in multi-robot systems (Speaker: Shinkyu Park)
• 17:00 ∼ 17:30 Discussions: Future research directions (Speaker: Organizers)

Organizers: Yongping Pan, Bowen Yi, Sayan Basu Roy, Alexey Bobtsov, Romeo Ortega

Location: Orchid Junior 4311

Website: https://cdc-acae.github.io/ 

Lecture Schedules: https://cdc-acae.github.io/program/

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. Nine distinguished speakers in adaptive control and estimation will join the workshop, including IEEE Fellows, IFAC Fellows, ASME Fellows, Editors-in-Chief of flagship journals in control, winners of the ASME Rufus Oldenburger Medal, as well as young rising stars. Our speakers and organizers come from a wide range of countries, including Australia, Belgium, Canada, China, France, India, UK, USA, Mexico, and Russia, which shows the diversity of perspectives and experiences that will be shared during the workshop. 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.

Organizers: Arpan Chattopadhyay, Kumar Vijay Mishra, John S. Baras, P. R. Kumar, Vivek S. Borkar

Location: Roselle Junior 4611

Website: https://sites.google.com/site/arpanchattop/cdc-2023-workshop

Abstract: Modern day inference and control theory has its origins in the 19th and early 20th centuries, with a growth of applications in the post-industrial-revolution’s automation and defence requirements. The 20th century witnessed the development of reinforcement learning techniques for control in uncertain environments, game theory to model multiple selfish or cooperating agents, stochastic filtering techniques for state tracking, networked control systems to extend the support of wireless networks to control systems, and machine learning techniques to infer from data. During the past two decades, rapid developments in multi-agent control, team decision theory, and big data analytics expanded the scope of these tools in the non-traditional areas. These theoretical developments happened in parallel with a massive improvement in the intelligence, sensing, communication, computation and storage capacities of the devices involved in inference and control systems. The increased affordability of autonomous systems has also led to the situation where autonomous systems owned by different entities need to interact with each other, often in an adversarial manner. Examples range from the competitive cognition and inverse cognition operations between a smart radar and an intelligent target, to the competition between a defender and a stealthy cyber-attacker, to a more futuristic scenario where two autonomous teams of UAVs fight each other in a battlefield. The inference and control operations in these systems need to be performed in uncertain environments. This necessitates the development of new theories of counter-adversarial inference, control and learning.

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.

Lecture Schedule:

• 09:00 ~ 09:30 Scalable control for distributed MIMO communications (Speaker: Soura Dasgupta, Univ. of Iowa)
• 09:30 ~ 10:00 Stealthy Sensor Attacks: Characterization and Moving Target Defense (Speaker: Henrik Sandberg, KTH) 
• 10:00 ~ 10:30 Unveiling the Impact: Exploring Grounding Effects on Scalable Consensus Networks (Speaker: Yamin Yan, HKUST)
• 10:30 ~ 11:00 Break
• 11:00 ~ 11:30 Multi-scale Network Games: Modeling, Analysis, and Control (Speaker: Mingyan Liu, University of Michigan, Ann Arbor)
• 11:30 ~ 12:00 Mean field game among teams (Speaker: Aditya Mahajan, McGill University)
• 12:00-13:30 Lunch
• 13:30-14:00 Robust Machine Learning, Reinforcement Learning and Autonomy: A Unifying Theory via Performance and Risk Tradeoff (Speaker: John. S. Baras, Univ. of Maryland)
• 14:00-14:30 Performance Guarantees for Learning based Decision Making (Speaker: Edwin K.P. Chong, Colorado State University)
• 14:30-15:00 Quickest detection of deception attacks in networked control systems with watermarking (Speaker: Subhrakanti Dey, Uppsala University)
• 15:00-15:30 Break
• 15:30-16:00 Deceptive Decision-Making: Inference, Strategies, and Environment Co-Design (Speaker: Melkior Ornik, University of Illinois at Urbana Champaign)
• 16:00-16:30 Reputation-based information design for increasing prosocial behaviour (Speaker: Rajesh Sundaresan, Indian
  Institute of Science Bangalore)
• 16:00-17:00 Adversarial Robustness Considerations in Black-Box (Speaker: Jonathan Scarlett, National University of Singapore)
• 17:00-17:30 Discussion session

Organizers: Thomas Beckers, Sandra Hirche, Rolf Findeisen

Location: Lotus Junior 4DE

Website: https://pilworkshop.tbeckers.com/

Lecture Schedule: Please visit https://pilworkshop.tbeckers.com/program

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.

Physics-informed machine learning leverages the inherent knowledge and understanding of the physical world to inform the learning process of machine learning algorithms. By explicitly integrating physical laws, domain expertise, and prior knowledge into the learning framework, physics-informed learning empowers control systems to leverage the flexibility and adaptability of machine learning while remaining grounded in a solid understanding of the underlying dynamics. This fusion enables more efficient and trustworthy learning that results in control applications with superior performance, robustness, and interpretability.

The potential applications of physics-informed machine learning for control and optimization are immense and diverse, spanning a wide range of domains. For example, in robotics, physics-informed learning can enhance the control of complex manipulators and autonomous agents by explicitly considering mechanical constraints, kinematics, and dynamics. Furthermore, in power systems and industrial processes, physics-informed learning can optimize control strategies by taking into account physical phenomena, such as heat transfer, fluid dynamics, and thermodynamics.

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.

Organizers: Lars Lindemann, Cristian Ioan Vasile

Website: https://sites.google.com/lehigh.edu/fmcdc2023

Location: Orchid Junior 4211

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. For this purpose, we have invited eight renowned expert speakers.

One of the primary objectives of the workshop is to identify key research challenges and opportunities in the areas of formal methods and decision making in the age of AI. Our speakers have been selected in this regard, and the workshop has a focus on:

• Control of perception-enabled systems: Perception-enabled systems are those that use high-dimensional sensors to make sense of the environment so that appropriate control actions can be taken. The workshop will popularize recent approaches for analyzing these systems, including statistical techniques for handling uncertainty under formal specifications.
• Scalable design of distributed systems: As AI becomes increasingly integrated into distributed systems, new methods are needed for designing and testing large-scale multi-agent systems. These design approaches need to be scalable and formally correct even in the face of agent failures and changing environmental conditions.
• Verifiable learning-enabled systems: As AI systems become more complex and autonomous, it becomes increasingly important to ensure and verifying that they are behaving as intended. Formal methods can be used to precisely formulate an intended system behavior, and new scalable formal verification tools need to be designed for verifying that high-dimensional learning-enabled systems are achieving their intended goals.
• Systems with humans in the loop: As humans become more integrated into AI systems, new approaches are needed for designing and evaluating feedback control loops that now incorporate robot and human feedback. The workshop will explore methods for ensuring that AI systems can effectively interact with humans. Formal specifications are close to structured language and present a convenient interface between humans.
• Safety in autonomy: One of the most critical challenges in the development of AI systems is ensuring their safety and reliability. There is a strong need for new methods for ensuring the safety of autonomous systems, including approaches for identifying and mitigating potential failures and developing backup systems in case of a failure.

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. To further exchange between our speakers and participants, we will have panel discussions.

With this workshop, we would also like to address a common misconception that formal methods in robotics and control do not scale and are hence not practical. Our expert speakers will showcase that we can now solve real-life problems and have reached a certain stage of maturity. Therefore, the workshop will also promote the use of formal methods and decision making techniques in real-world applications, including autonomous driving and robotic systems.

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.

Lecture Schedule:

Call for Short Abstract Submissions: http://tiny.cc/cdc23-ws-abstract

Organizers: Angela P Schoellig, Jonathan P. How, Peter Corke, George J. Pappas, Sandra Hirche, Lukas Brunke, Siqi Zhou, Adam W. Hall, Federico Pizarro Bejarano, Jacopo Panerati

Location: Melati Junior 4111

Website: https://www.dynsyslab.org/cdc-2023-workshop-on-benchmarking-reproducibility-and-open-source-code-in-controls/

Lecture Schedule: https://www.dynsyslab.org/cdc-2023-workshop-on-benchmarking-reproducibility-and-open-source-code-in-controls/#program 

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.

Organizers: Alexandre Bayen, Karthik Gopalakrishnan, Devansh Jalota, Jessica Lazarus, Marco Pavone

Location: Melati Junior 4011

Website: https://sites.google.com/stanford.edu/l4sat2023

Lecture Schedules: https://sites.google.com/stanford.edu/l4sat2023/schedule

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.

Learning-based approaches have the potential to address these issues and help extend the capabilities of traditional control algorithms for transportation applications; however, the use of data-driven techniques for the societally aware operation of emerging mobility systems is largely untapped. The objectives of the workshop are four-fold:

1. 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.
2. Provide a platform to explore new research directions geared towards tapping the full potential of data in achieving societal objectives in transportation systems.
3. 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.
4. 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.

Organizers: Jr-Shin Li, Shen Zeng, Hiroya Nakao

Location: Roselle Junior 4612

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. A common thread of these very challenging dynamic large-scale population problems lies in the fundamental limitations that control and observation can only be implemented at the population level, i.e., through broadcasting a common input signal to all the systems in the population and through receiving aggregated measurements (e.g., snapshots or images) of the systems in the population, respectively. This restriction gives rise to a new control paradigm of population-based control, called ensemble control.

Interest in conducting cutting-edge analysis, estimation, control, and learning algorithms and technologies capable of addressing these emerging sophisticated control systems has seen a stellar growth in recent years, and the development is continuously ongoing. 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.

Lecture Schedule:

Website: TBD

Organizers:  Sivaranjani Seetharaman and Apurv Shukla

Location: Roselle Junior 4613

Website: https://sites.google.com/tamu.edu/cdc-2023-workshop/home

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.

We have an exciting slate of experts on this topic. The confirmed talks will cover a wide range of advances including new control strategies for the optimal and safe operation of renewable-rich grids, co-optimization algorithms for management of EVs, heavy-duty, and hydrogen-fueled vehicles, learning-based control and optimization of DERs for demand response at the grid edge, and market/incentive designs for safe operation at the transportation-energy nexus.

Organizers: Lihua Xie, Ben M. Chen

Location: Orchid Main 4201AB

Website: http://www.mae.cuhk.edu.hk/~usr/workshops/cdc2023/

Lecture Schedules: http://www.mae.cuhk.edu.hk/~usr/workshops/cdc2023/Schedule.html

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.

Organizers: Gennaro Notomista, Yorai Wardi

Location: Orchid Main 4301AB

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.

Lecture Schedule: 

9:00 – 9:10                  Welcome.

9:10 – 9:40                  Magnus Egerstedt.  Mutualistic Interactions in Heterogeneous Multi-Robot Systems. 

9:40 – 10:30                Ryan Cosner.  CBF-based Safety for Real-World Robotic Systems.

10:30 – 11:00              Coffee break.

11:00 – 12:00              Erlend A. Basso and Aurora Haraldsen.  

Guidance schemes for underactuated marine vehicles with safety guarantees using control barrier functions.

12:00 – 1:30                Lunch break.

1:30 – 2:30                  Christos Cassandras.  

Guaranteed Safe Autonomous Cooperative and Uncooperative Multi-agent Systems with Control Barrier Functions.

2:30 – 3:15                  Takeshi Hatanaka.   Constraint-based control of multi-drone systems towards smarter agriculture.

3:15 – 3:40                  Coffee break.

3:40 – 4:30                  Kyriakos Vamvoudakis.   Fixed-Time Convergent Safe Reinforcement Learning.

4:30 – 5:20                  Yorai Wardi.   Integral CBF for dynamic controllers and tracking applications.

5:20 – 6:00                  Conclusions and discussion.

Lecture Abstracts: Click to Download

Organizers: Rong Su, Xiang Yin

Location: Orchid Junior 4212

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.

Resilience has emerged as a property of major interest for the design and analysis of a complex system. It describes the system ability to continue providing its designed services or functions, even after disruptive changes in the system, caused either by faults, or other naturally occurring phenomena, or by malicious actions. Formal methods in resilience has been enjoying a spotlight in many different fields, including the Discrete Event Systems (DES) community, hybrid systems community and computer science community. 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:

• 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.
•  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.

Overall, in this workshop, we intend to achieve the following two goals:

(1) To report and showcase recent technical developments related to formal methods in system resilience; and

(2) to identify challenges ahead which, although hindering the current research efforts, are critical for safety-critical CPS, in order to arouse more interests and efforts at a broader societal level to ensure R&D sustainability.

Lecture Schedule: Click here to download

Website: TBD

Organizers: Shandong University of Science and Technology (Platinum Sponsor)

Location: Roselle Junior 4711

Abstract: 

The development of control and optimization spans several decades and has evolved through significant contributions from various fields, including mathematics, engineering, economics, and operations research. Today, control and optimization continue to advance with the integration of networks, artificial intelligence, and machine learning. They are applied to address complex real-world problems across diverse domains while encountering some emerging challenges. To tackle these challenges, some new theories, methodologies, and algorithms are constantly being developed. The workshop is on recent advances of the related new theories, methodologies, and algorithms. The aim is to brainstorm on a couple of focused topics related to learning-based control and multiagent systems but not limited to these topics.

Lecture Schedule:

Organizers: Shanghai Jiang Tong University (Platinum Sponsor)

Location: Simpor Junior 4811

Date and Time: 14:30-17:00, December 12, 2023

Download Flyer: Shanghai Jiaotong University Workshop

Abstract: 

This workshop is a retrospective celebration of the rich legacy and advancements in the field of Systems and Control at Shanghai Jiao Tong University (SJTU). Over the past six and a half decades, SJTU has been at the central place in China for pioneering research, innovation, and academic excellence in Systems and Control.

The workshop will commence with a historical overview, highlighting key milestones, breakthroughs, and the evolution of Systems and Control research at SJTU. Meanwhile, the workshop will also cast a visionary gaze into the future.

Emphasis will be placed on strategies for recruitment, fostering collaborations, and
cultivating talent in the evolving landscape of Systems and Control.

Lecture Schedule:

More Talks from SJTU Alumni and our Collaborators will be announced.

Join us in commemorating the remarkable journey of Systems and Control at SJTU and charting the course for its vibrant future.

Especially welcome young scholars to discuss potential position and development in SJTU.

If you have any questions, please contact us via email.
E-mail: autodept@sjtu.edu.cn