|59th Conference on Decision and Control - Jeju Island, Republic of Korea - December 14th-18th 2020|
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Two-Day Workshops1:00PM - 5:00PM, UTC (Coordinated Universal Time), December 12 & 13 (Sat & Sun), 2020
One-Day Workshops1:00PM - 5:00PM, UTC (Coordinated Universal Time), December 13 (Sun), 2020
T1. Advanced Battery Management: Recent Advances and Future Innovations
Organizers: Huazhen Fang (University of Kansas), Xinfan Lin (University of California Davis), Scott Moura (University of California, Berkeley), Simona Onori (Stanford University)
Abstract: Battery energy storage systems are rising as the backbone of numerous industrial and civilian systems, while playing a key role in moving the world into a clean energy era. Their performance and safety of critically rely on advanced battery management, which has attracted considerable research, particularly from the systems and control community, in the past decade. The growing efforts have led to tremendous progresses in leveraging control theory to enable complex, high-performing battery systems in a broad range of application domains. The developments in turn continuously stimulate exciting insights into emerging challenges. This two-day workshop is thus proposed to gather veteran researchers in this vibrant field to share up-to-date advances and perspectives about future innovations. It also aims to foster a creative space for open discussions among participants, which will spark innovative ideas and inspirations about future control-theory-driven battery management.
The workshop will feature more than ten speakers working extensively in this field and representing diverse backgrounds across academia and industry. The talks will cover various key dimensions of this field, highlighting a confluence of electrochemical modeling, control theory, machine learning and applications.
The target audience of the proposed workshop includes graduate students, researchers and professional engineers from academic institutions, companies, and industrial and government laboratories, who want to have an exposure to the cutting-edge developments, new trends and open challenges in the field of battery management.
More Details: TBA
T2. Real time NMPC - From Fundamentals to Industrial Applications
Organizers: Stefano Longo (Embotech AG), Thivaharan Albin (Embotech AG), Craig Buhr (MathWorks Inc.)
Abstract: In this workshop we would like to give an overview on Nonlinear Model Predictive Control development concentrating on the industrial perspective. At the beginning an overview on the topic of real-time Nonlinear MPC is given. Based on this, challenges are outlined for developing NMPC algorithms for serial deployment. Along with that some best practices and state-of-the-art tools are presented that facilitate the design. Finally, several success stories for application of Nonlinear MPC to real-world systems are given. They range from autonomous driving to the field of robotics. The presenters from Embotech and MathWorks will highlight some use cases and experience from their industrial work.
The workshop is planned as an 8-hour workshop (4hrs Sat. + 4hrs. Sun). On saturday we would pre-record the videos and then offer Q&A sessions. For sunday we would first do a session with pre-recoded videos. Additionally, we would hand-out some programming exercises and assist during all the time via Zoom.
More Details: TBA
T3. Data-driven Control
Organizers: Kanat Camlibel (University of Groningen), Harry Trentelman (University of Groningen), Henk van Waarde (University of Groningen), Jaap Eising (University of Groningen)
Abstract: A great deal of the mainstream systems and control theory based on the assumption that the mathematical model of the to-be-controlled system is known. An alternative to the model-based approach is to design feedback controllers by using data that are collected from the to-be-controlled system. Data-driven approach gains more and more popularity as the growing complexity of engineering systems makes obtaining accurate mathematical models from first principles more and more difficult.
This workshop aims at portraying the state-of-the-art in data-driven control. In order to provide a significant coverage of the area, we propose a two-day workshop with 12 lectures that address diverse topics from linear systems to nonlinear systems and from robust control to predictive control. The diversity of the covered topics, speakers and audience is expected to initiate cross-fertilization of ideas.
The proposed workshop targets a broad audience from graduate students and researchers looking for an introduction to a new and active area of research to practitioners interested in data-driven design methods. The required background is basic familiarity with systems and control theory as well as optimization. Although the lectures address various different topics, they are closely related to each other in their spirit as well as approach.
More Details: TBA
T4. Dynamics in Social and Economic Networks
Organizers: Wenjun Mei (ETH Zurich), Francesca Parise (Cornell University), Ming Cao (University of Groningen), Giacomo Como (Politecnico di Torino), Bahman Gharesifard (Queen's University)
Abstract: Network effects are pervasive in our society and affect many aspects of our life from how we acquire information, how we interact, how we make decisions and what opportunities we are exposed to. This is even more the case since the rise of Internet and online social media, which do not only provide a vast number of empirical data for the quantitative study of social systems, but have also deeply changed the pattern of how people interact with each other. In this era of information revolution and dense interactions, our society faces various unprecedented challenges with profound impacts on modern politics and economy, such as opinion polarization, the politicalization of public debates, the effects of echo chambers and filter bubbles. Phenomena such as spreading of contagion (may this be of pathogen or misinformation), coordination of strategic behavior as well as targeted interventions and incentive for efficient use of resources over networks are rapidly becoming of fundamental importance for both the society and its economy. Mathematical modeling plays a fundamental role in understanding how these macroscopic phenomena emerge from certain microscopic mechanisms of social interactions and certain network structures.
Exploiting the progress in complex networks and data mining, the last decades have witnessed a rapid development of the research on the statistical and static features of social networks, in the framework of Social Network Analysis. However, dynamical processes on/of social networks, which are directly related to the aforementioned phenomena, remain to be thoroughly studied. Due to the rapid progress in the study of multi-agent systems, researchers on control theory have recently contributed various useful mathematical tools to the study and control of social network dynamics. These mathematical tools make it possible for us to investigate some fundamental or emerging problems in social science, including: (1) What is the "main factor" that governs opinion dynamics and what mechanisms could drive public opinions to polarization? (2) How do social media and online recommendation system shape the public opinion formation processes? (3) Is there any efficient way to mitigate the spreading of misinformation or the impact of malicious opinion manipulation? (4) How does network structure influence individual's strategic behavior on social networks? (5) How can one plan targeted interventions or incentive to maximize system performance by exploiting such network effects?
We thus believe this is a perfect timing to bring these socio-economic questions to the attention of the broad audience of control theorists. We organized the workshop by bringing together researchers that work on this rapidly expanding area using different approaches, as for example game theory, complex network analysis, and multi-agent systems so that attendees can have a broad overview of the different techniques that one can use to answer the questions above. The aim is to give a general introduction to dynamics on socio-economic systems, as well as present the latest results on various emerging topics such as social learning and opinion dynamics, network propagation models, coordination of competitive network systems, information design, and interventions under partial information.
More Details: TBA
T5. Non-linear and adaptive control: A tribute to Laurent Praly for his 65th birthday
Organizers: Christophe Prieur (CNRS), Zhong-Ping Jiang (New York University)
Abstract: This workshop is dedicated to Laurent Praly's 65th birthday and to honor his long-lasting pioneering contributions to multiple topics in control: linear adaptive control, nonlinear adaptive control, Lyapunov design, input-to-state stability and stabilization, output feedback control, nonlinear observers, stabilization. The workshop is comprised of six talks by Laurent's colleagues who will speak about the state of the art and progresses in various important topics in the field of systems and control theory. A panel composed of three editors-in-chief of the top-tier journals IEEE Transactions on Automatic Control, Automatica and Systems & Control Letters will discuss future challenges of control theory. It is expected that the workshop serves as a platform for stimulating discussions among researchers and practicing engineers and will inspire a next generation of students to enter the fascinating field of nonlinear and adaptive control.
More Details: TBA
O1. Control, Optimization, and Learning Methods for Emerging Mobility Systems
Organizers: Andreas A. Malikopoulos (University of Delaware), Christos G. Cassandras (Boston University)
Abstract: Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. Emerging mobility systems are typical cyber-physical systems where the cyber component (e.g., data and shared information through vehicle-to-vehicle and vehicle-to-infrastructure communication) can aim at optimally controlling the physical entities (e.g., CAVs, non-CAVs). The cyber-physical nature of such systems is associated with significant control challenges and gives rise to a new level of complexity in modeling and control. As we move to increasingly complex emerging mobility systems, new control, optimization, and learning approaches are needed to optimize the impact on system behavior of the interplay between vehicles at different traffic scenarios. It is expected that CAVs will gradually penetrate the market, interact with non-CAVs and contend with vehicle-to-vehicle and vehicle-to-infrastructure communication limitations, e.g., bandwidth, dropouts, errors and/or delays. New system approaches are needed to accommodate the challenges associated with the partial penetration of CAVs and communication limitations.
The workshop intends to stimulate a discussion about a feasible research roadmap at the intersection of control, optimization, and learning that would result in new approaches for addressing the following technical challenges in emerging mobility systems. First, any potential limitations in the information (e.g., bandwidth, dropouts, and errors or delays) that CAVs receive from each other and the infrastructure could have a major impact on the performance. Second, different CAV penetration rates can significantly alter mobility system efficiency. Third, managing online vehicle-level operation for the controller can involve significant computational challenges. Finally, improving robustness and safety of CAVs constitutes a major technical challenge which has attracted considerable attention.
More Details: TBA
O2. Compressed Sensing and Sparse Representation for Systems and Control
Organizer: Masaaki Nagahara (University of Kitakyushu)
Abstract: In this workshop, we will review recent advances of compressed sensing and sparse representation for systems and control. Compressed sensing and sparse representation have been receiving a lot of research attention in machine learning, signal processing, and statistics. In recent years, researchers have become increasingly interested in compressed sensing and sparse representation for systems and control.
Sparsity is one of the major topics in machine learning and signal processing. Compressed sensing, also known as sparse representation, refers to the recovery of a high-dimensional but low-complexity vector (or signal) from a limited number of measurements. The notion of sparsity has also been attracting attention in control systems. In control systems, the sparsity in time is proposed for resource-aware control, such as event- (or self-) triggered control, where sensing and actuation is performed when needed. Also, optimal control called maximum hands-off control directly minimizes the time duration on which the control is active (i.e. L_0 norm). Sparsity is also available for model reduction of control systems and networks.
In this workshop, we will review recent advances of sparsity methods in systems and control, and communications. We give lectures on (1) tradeoffs between performance and complexity in control, (2) L_0 optimal control and control node scheduling, (3) sparsity methods for wireless communications, and (4) maximum hands-off control.
More Details: TBA
O3. Learning and Security for Multi-Agent Systems
Organizers: Hideaki Ishii (Tokyo Institute of Technology), Quanyan Zhu (New York University)
Abstract: Machine learning provides a set of useful analytic and decision-making tools for a wide range of applications, including signal processing, vision-based robotics, and data-driven control systems. Security research aims to address the issue of protecting networks from adversarial behaviors. Despite that the two communities focus on different problems, the intersections between learning and security have received a lot of attention. The connections between the two are two-fold. First, security of learning is the center of recent advances in adversarial learning problems. Recent years have witnessed a growing number of adversarial attacks and malicious behaviors aimed at systems built with machine learning and optimization algorithms. There is a need for new theories and models to provide a fundamental understanding of the vulnerabilities of these algorithms and develop methods to safeguard the system from attacks. Second, the growing volume and diversity of data for computing systems have created opportunities to improve the system security and resilience through learning. Learning for security addresses these challenges by developing novel adaptive and data-driven techniques based on ideas and concepts from decision and control theory.
The confluences between security and learning become more apparent and essential for multi-agent systems. Malicious agents can manipulate and mislead the learning by disseminating misinformation and poisoning the data. Data-driven methods are needed to detect such behaviors over the network and prevent cascading failures and mitigate systemic risks. Understanding of the confluences in multi-agent systems will advance federated learning in adversarial settings, designing adaptive cyber defense, and improving the security of cyber-physical systems and the Internet of Things. This workshop will bring together experts from the cybersecurity, machine learning, and control communities to highlight recent works that contribute to addressing these challenges. Our agenda will feature invited talks and a joint academia and funding agency panel discussion to identify open research problems that will be of interest to the broader community.
More Details: TBA
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