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58th Conference on Decision and Control - Nice, France - December 11th-13th 2019

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Workshops

The CDC 2019 is offering 11 full-day and 3 half-day pre-conference workshops on Tuesday, December 10, 2019, addressing current and future topics in control systems from experts from academia, research institutes, and industry.

All workshops are confirmed.

Participants are required to register. See the Registration webpage for fees and details.

Questions can be directed to the Workshop Chair, Prof. Ilya Kolmanovsky (ilya@umich.edu).



List of Workshops Offered at the 58th CDC

Workshops Description

Verifiable Adaptive Control Systems and Learning Algorithms - FULL DAY

Organizers: Tansel Yucelen (University of South Florida), Anuradha Annaswamy (Massachusetts Institute of Technology), Warren Dixon (University of Florida), K. Merve Dogan (University of South Florida), Jonathan A. Muse (Air Force Research Lab), and Frank Lewis (University of Texas at Arlington)

Time and Location: Full day, Dec. 10, 8:30-17:30 - Galliéni 4

Abstract: A fundamental problem in the design of feedback control architectures is to achieve closed-loop system stability, performance, and robustness against exogenous disturbances and system uncertainties. Unlike fixed-gain control architectures, adaptive control systems offer the capability to deal with exogenous disturbances and system uncertainties, in an online fashion, through learning. This implies that they are not tuned to a worst-case scenario and they continuously improve their performance in real-time. These two appealing aspects make adaptive control systems and learning algorithms important candidates for a wide array of physical systems. Although government and industry agree on their potential in providing vehicle safety and reducing vehicle development costs, a major issue is the lack of system-theoretic methods for their verification, due to their nonlinear nature. Motivated by this standpoint, the objective of this full-day workshop is to cover the state-of-the-art verifiable system-theoretic approaches in adaptive control systems and learning algorithms for their safe and reliable real-world applications. Specifically, the presenters of this workshop will cover topics addressing how to implement adaptive control systems with verifiable transient and steady-state performance guarantees, how to address the presence of actuator and unmodeled dynamics when adaptive control systems are in feedback loops, how to design and analyze adaptive control systems for physical plants with switching modes, and how to advance adaptive control systems with system-theoretic guarantees using tools and methods from machine and reinforcement learning. This workshop will be relevant to practicing professionals from electrical, mechanical, and aerospace industries. It also intends to cultivate new future research directions under a panel discussion involving organizers and expected workshop attendees. Finally, this workshop is expected to be a great value to experts and students in the adaptive control systems and learning algorithms fields.

Webpage: http://lacis.eng.usf.edu/page6/index.html

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Mathematical Theory of Control and Signal Processing in the Digital World (A workshop dedicated to Yutaka Yamamoto's 70th birthday) - FULL DAY

Organizers: Masaaki Nagahara (The University of Kitakyushu), Hideaki Ishii (Tokyo Institute of Technology), Kenji Kashima (Kyoto University), Kenji Sugimoto (Nara Institute of Science and Technology)

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée A1

Abstract: This workshop is organized to celebrate Professor Yutaka Yamamoto’s 70th birthday and honor his long-lasting contributions to mathematical theory of control and signal processing. This workshop will bring together his colleagues who will present a broad range of topics related to control and signal processing for the digital world. In particular, the speakers will present talks on robust control, stochastic systems, signal processing, and system identification. The goal of this workshop is to inspire a future generation of researchers.

Webpage: http://www.sc.dis.titech.ac.jp/yy_workshop_cdc19/

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Model Predictive Control: from the Basics to Reinforcement Learning - FULL DAY

Organizers: Alberto Bemporad and Mario Zanon

Time and Location: Full day, Dec. 10, 8:30-17:30 - Galliéni 7

Abstract: In spite of its long tradition of success as a very powerful and versatile advanced control technique, the interest of industry and academia in model predictive control (MPC) is strongly growing, and MPC is spreading to a large variety of application domains. While most of the attention has been focused so far on computational efficiency and closed-loop performance, as the use of MPC in industrial production is increasing the time required to develop an MPC solution has also become of strong importance. Development time is mainly due to constructing suitable prediction models and to calibrating the resulting controller. Reinforcement learning, and more generally data-driven synthesis of MPC laws, has recently attracted a lot of attention to possibly reduce such development time. This workshop aims at providing an overview of several techniques for practical use of MPC, covering linear, hybrid, and nonlinear MPC formulations and various computational methods that can be used to effectively compute the MPC action in real-time. The workshop also aims at bringing the attendee towards understanding emerging reinforcement learning and policy search methods for tuning MPC controllers directly from data for reduced design and calibration effort. Emphasis will be given to understanding the necessary theoretical background that lead to the successful implementation of MPC in practice, addressing advantages and potential difficulties. During the workshop pointers towards dedicated software will be given, so that the attendee will be able to not only properly formulate the problem, but also to solve it using state-of-the-art tools. The workshop is organized as a tour, starting from the most basic and standard formulations based on deterministic linear systems with quadratic costs, and following the road towards more advanced formulations, including hybrid, stochastic, nonlinear, and economic MPC. The last part of the workshop will be dedicated to presenting promising results in data-driven learning of control laws that have a great potential of use in MPC, with the intention of also triggering further research ideas in the audience. A few practical case studies will be described so as to also motivate the practical and industry-oriented flavor of the workshop.

Webpage: http://dysco.imtlucca.it/mpc-cdc19

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Uncertainty Synthesis - HALF DAY

Organizers: E. Bakolas, Y. Chen, T. Georgiou, P. Tsiotras

Time and Location: Half Day, Dec. 10, 8:30-12:30 - Galliéni 6

Abstract: All dynamical systems are prone to exogenous disturbances, and the uncertainty introduced by these exogenous disturbances propagates along with the system states. More often, the amount of uncertainty in the system grows with time as the system evolves and, consequently, controlling the uncertainty is of paramount interest to maintain a certain level of performance. This is especially true when one needs to design optimal controllers, which are known to be susceptible to modeling errors. Recent advances have it possible to directly quantify and control the uncertainty of a dynamical system. Controlling the uncertainty of a dynamical system implies the ability to control the state distribution over time, a problem that has many applications, including image segmentation, ensemble and swarm control, control of particle beams, neuronal ensembles, and many others — in addition to just reducing the uncertainty in a feedback system. The objective of this workshop is twofold: the first objective is to report on current advances in the area of uncertainty quantification and control to enable resilient and robust operation of dynamical systems and swarms of robots; the second objective is to bring together - in the same room - outstanding researchers from leading institutions who have contributed on this topic over the years.

Webpage: http://uncertainty-synthesis-workshop.ae.gatech.edu/

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Learning, Games and Control for Security of Cyber-physical Systems - FULL DAY

Organizers: Quanyan Zhu (New York University) and Radha Poovendran (University of Washington)

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée 1

Abstract: The topic of this workshop is the control and secure operation of cyber-physical systems (CPSs) using perspectives from game theory and machine learning. Cyber-physical systems are complex entities where the working of a physical system is governed by its interactions with computing devices and algorithms. These systems are ubiquitous. Examples include medical devices and robots on a small scale, to power systems and connected communities on a large scale. CPSs are expected to operate in dynamically changing environments, which could result in it being the target of malicious attacks that aim to prevent it from accomplishing a goal. Strategies to mitigate the effect of an attack must take into consideration the fact that adversaries are often stealthy, intelligent, and persistent. This workshop will feature talks by leading experts whose recent work uses game theory and data-driven approaches to model and analyze the security of CPSs. The workshop also plans to feature a presentation by a representative from a funding agency, and a panel discussion in order to identify open research problems that will be of interest to the broader community.

Webpage: https://wp.nyu.edu/quanyan/cdc-2019-workshop/

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Resilence and Controllability of Large Scale Systems: A Network-Theoretic Approach - FULL DAY

Organizers: Mohammad Pirani, Shreyas Sundaram, and Victor Preciado

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée 2

Abstract: Description: Large-scale systems play a central role in a multitude of applications, from power grids and smart buildings to aerospace systems, swarm robotics, social networks, and intelligent transportation systems. As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of controllability, observability, and resilience of such networks increase in importance. The need to redefine classical system and control theoretic notions into the language of networks has recently started to gain attention as a fertile and important area of research. A key challenge for the controls community is thus to understand how to leverage network theory along with systems and control to analyze the controllability, observability, and resilience of large-scale interconnected systems. The IEEE Conference on Decision and Control, as one of the premier annual gatherings of researchers in the field of systems and control, is a perfect venue for a workshop on network-theoretic approaches to controlling large scale systems. The goal of this workshop is to present the challenges in this area, together with tools and approaches that have been recently developed to address this problem. In particular, the key emphasis of this workshop will be on the use of graph-theoretic approaches to large-scale systems analysis, which will differentiate it from other workshops on control and security of centralized systems. The target audience is students, researchers and practitioners from academia and industry who are interested in learning about (and contributing to) the emerging field of network control systems. The workshop will be highly interactive and will feature tutorial-style talks by leading experts in the field, giving the audience a perspective of how network theory plays a role in the resilience and control of large scale systems, and how to best combine different perspectives to develop efficient, reliable and resilient systems.

For more information and complete list of speakers, please see workshop website: https://sites.google.com/view/workshopcdc2019/home

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Learning, Decision, and Control over Networks - HALF DAY

Organizers: Vaibhav Srivastava (Michigan State University) and Fabio Pasqualetti (University of California, Riverside)

Time and Location: Half Day, Dec. 10, 8:30-12:30 - Méditerranée A3

Abstract: From electric power grid to biological systems to massive transportation systems, socio-technological networked multi-agents systems are ubiquitous across scientific disciplines. In the era of big data, understanding the interplay of learning, decision-making, and control in distributed control of such network systems in vital. Such understanding will empower the future technology to leverage the plethora of data is a systematic and efficient fashion. To this end, a half day workshop is organized that will bring together experts in this area to present the state-of-the-art and discuss future research directions.
This half-day workshop will feature presentations and discussions from experts in the areas of networked multiagent systems. The speakers are:

  1. Jorge Cortes, University of California at San Diego
  2. Sonia Martinez, University of California at San Diego
  3. Giuseppe Notarstefano, University of Bologna
  4. Ketan Savla, University of Southern California
  5. Stephen L. Smith, University of Waterloo
  6. Shaunak D. Bopardikar, Michigan State University

Webpage: https://www.egr.msu.edu/~vaibhav/cdc2019workshop.html

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Spatio-Temporal Reasoning for Control of Cyber-Physical Systems - FULL DAY

Organizers: André de Matos Pedro and Laura Nenzi

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée C4

Abstract: This workshop aims to present the most recent advances in the development of logic-based procedures for the analysis and control of spatially distributed Cyber Physical Systems (CPS), with particular emphasis on the combination of temporal and spatial behaviors. Spatially distributed CPS, such as robotic swarms and smart environments, often exhibit multiple and unpredictable behaviors that increase the efforts needed in their analysis. Studying and controlling such systems requires a growing demand for efficient tools capable of dealing with such complex behavioral patterns.
Spatio-temporal logic is an innovative way to reason and face such challenges. This workshop has the dual objective: (1) showing the usefulness of spatio-temporal logic to the control community in the context of spatially distributed CPS and (2) highlighting what are the main important challenges in the analysis of such systems that logic community can help to solve in the near future. Several case studies will be considered to discuss the real usefulness of these methodologies. This will lay the foundations for a verification framework of spatially distributed CPS as well as fill the gap between theory and practice of CPS design, deployment and testing, with particular emphasis in the decision procedures and monitoring mechanisms.

For more information, including the list of speakers, please see workshop website: http://strcc.isp.uni-luebeck.de

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Neuroscience and Control: the Emerging Intersection - FULL DAY

Organizers: Sergio Pequito (Rensselaer Polytechnic Institute) and Alexander Medvedev (Uppsala University)

Speakers: Sergio Pequito (Rensselaer Polytechnic Institute), Erfan Nozari (University of California, San Diego), John Doyle (CalTech), Arian Ashourvan (University of Pennsylvania), Tim Denison (Oxford University), Alexander Medvedev (Uppsala University), and Miroslav Pajic (Duke University).

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée A2

Abstract: The last years have witnessed a fast development of models, tools, and experiments aimed at understanding neural circuitry and brain dynamics. The proposed workshop will bring together researchers from different backgrounds to demonstrate how the theory of dynamical systems and control engineering successfully enable new insights into neuroscience and emerging neural technology. More specifically, the scope of the talks covers such topics as mathematical modeling and analysis of neural populations, intracranial electrical stimulation in rehabilitation technology and prosthetics, brain-machine interfaces, and uncovering the drivers of brain activity. We propose to not only present and address some of the fundamental problems in this research area but also to raise more questions for future research within the controls community. Subsequently, we believe that these sessions will have a profound effect on our understanding of brain dynamics and actuation mechanism. A healthy mixture of theoretically oriented talks with more applied ones is proposed, thus maximizing the relevant audience, and attracting new researchers in these exciting problems, creating a larger yet focused community.

Webpage: https://sites.google.com/site/neurocontrolcdc19/home

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Model Predictive Control of Hybrid Dynamical Systems - FULL DAY

Organizers: Berk Altın and Ricardo G. Sanfelice

Time and Location: Full day, Dec. 10, 8:30-17:30 - Galliéni 1

Abstract: Hybrid systems model the behavior of dynamical systems where the states can evolve continuously as well as instantaneously. Such systems arise when control algorithms that involve digital devices are applied to continuous-time systems, or due to the intrinsic dynamics (e.g. mechanical systems with impacts, switching electrical circuits). Hybrid control may be used for improved performance and robustness properties compared to conventional control, and hybrid dynamics may be unavoidable due to the interplay between digital and analog components of a system.
This workshop is a complete course on the analysis and design of model predictive control (MPC) schemes for hybrid systems. It presents recently developed results on asymptotically stabilizing MPC for hybrid systems based on control Lyapunov functions. The workshop provides a detailed overview of the state of the art on hybrid MPC, and a short tutorial on a powerful hybrid systems framework (hybrid inclusions) that can model hybrid dynamics described in other frameworks (e.g. switched systems, hybrid automata, impulsive systems). Key analysis tools in this setting are demonstrated, along with several advantages over other frameworks. This background is then used to lay the theoretical foundations of a general MPC framework for hybrid systems, with guaranteed stability and feasibility. The ideas are illustrated in several applications.
The workshop targets a broad audience in academia and industry, including graduate students, looking for an introduction to an active area of research and some modern mathematical analysis tools; control practitioners interested in novel design techniques; researchers in dynamical systems in pursuit of relevant applications; and researchers in industry and labs applying hybrid predictive control methods to engineering systems. The required background is basic familiarity with continuous- and discrete-time nonlinear systems. The lectures are closely related to each other and not meant to be independent research presentations.

Webpage: https://hybrid.soe.ucsc.edu/hybridmpccdc19

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Lagrangian Control for Traffic Flow Smoothing in Mixed Autonomy Settings - FULL DAY

Organizers: Alexandre Bayen, George J. Pappas, Benedetto Piccoli, Daniel B. Work, Jonathan Sprinkle, Maria Laura Delle Monache, Benjamin Seibold, Cathy Wu, Abdul Rahman Kreidieh, Eugene Vinitsky, Yashar Zeiynali Farid

Time and Location: Full day, Dec. 10, 8:30-17:30 - Galliéni 2

Abstract: The field of transportation is undergoing profound and rapid disruptions, led in part by revolutions in automation, electrification, and data science / machine learning. In particular, the rapid emergence of autonomous vehicle (AV) technology and its potential as a means of Lagrangian control has led many to ask the question: How can AVs in the presence of human-driven vehicles improve the flow of traffic? In order to shed some light on this topic, this workshop discusses the mathematical, engineering, and technological advances in a group of fields that are steadily enabling vehicle automation as a viable means of traffic flow control.

  1. Means Field Models and Traffic Aggregation: The complexity of the traffic flow dynamics (e.g. multi-lane dynamics, merges, ramps, non-FIFO assumptions) necessitates the use of abstraction models to overcome the complexity of the dynamics of single agents (vehicles), which make full analytical approaches nearly intractable. We present advances in systematic approaches to aggregate (human-driven) traffic flow actuated by Lagrangian controllers (AVs), via mean field equations and coupled PDE-ODE systems.
  2. Deep Reinforcement Learning (RL): Recent years have seen RL emerge as a promising framework for control of complex dynamical systems. This is particularly appealing in the context of traffic, which itself exhibits the rich, complex behaviors. We present techniques for applying scalable RL techniques to mixed-autonomy traffic. This includes topics such as decentralization, and methods for generating policies that are transferable to actual networks.
  3. Verification of Deep Neural Networks (DNNs): The rise of deep RL as a means of control has been treated with some skepticism, attributed in part to the black-box nature of DNNs. In a setting where humans and actuated devices are expected to interact with one another, this serves as a significant barrier to deployment. In response to this, we present techniques for verifying the safety properties of DNNs using algorithms for satisfiability modulo convex optimization.

Webpage: https://flow-project.github.io/tutorial.html#cdc2019

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Finite-, Fixed-, and Prescribed-Time Stabilization and Estimation - FULL DAY

Organizers: Denis Efimov, Miroslav Krstic, Wilfrid Perruquetti, Andrey Polyakov, Drew Steeves

Time and Location: Full day, Dec. 10, 8:30-17:30 - Galliéni 3

Abstract: The goal of this workshop is to present recent advances on the design and analysis of control and estimation algorithms with accelerated convergence rates. The focus is to exhibit algorithms which ensure finite-, fixed- or prescribed-time convergence. The associated approaches and related properties that will be covered include: homogeneity, the implicit Lyapunov function method, time-varying damping, and discretization tools for highly nonlinear systems. Recent interest in these more demanding types of stability are due to emerging applications (e.g., flying robots, cyber-physical systems) which have strict performance requirements regarding convergence rate, robustness and scalability. Conventional control and estimation methods fail to meet these demands. As such, the aforementioned approaches have been developed or extended to meet these strict targets, and will be at the forefront of this workshop.

Webpage: https://team.inria.fr/valse/fr/full-day-workshop-finite-fixed-prescribed-time-stabilization-and-estimation-ieee-cdc-2019/

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Computational optimal transport for applications in control and estimation - HALF DAY

Organizers: Yongxin Chen, Tryphon T. Georgiou, Johan Karlsson, Axel Ringh, Francois-Xavier Vialard

Time and Location: Half Day, Dec. 10, 13:00-17:30 - Méditerranée A3

Abstract: The optimal mass transport problem is a classical problem in mathematics, and dates back to 1781 and work by Gaspard Monge where he formulated an optimization problem for minimizing the cost of transporting soil for construction of forts and roads. Historically the optimal mass transport problem has been widely used in economics in, e.g., planning and logistics, and was at the heart of the 1975 Nobel Memorial Prize in Economic Sciences. In the last two decades there has been a rapid development of theory and methods for optimal mass transport and the ideas have attracted considerable attention in several economic and engineering fields. These developments have lead to a mature framework for optimal mass transport with computationally efficient algorithms that can be used to address problems in the areas of systems, control, and estimation.
This workshop is being organized in order to introduce optimal transport to a larger audience in the CDC community. The main goal of this workshop is to give a tutorial of it, regarding both theoretical and computational aspects, and to present some applications in the areas of control and estimation.

Webpage: https://people.kth.se/~johan79/Workshops/OMT_CDC_2019/

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Systems and Control for Smart Society and Cyber-Physical and Human Systems - FULL DAY

Organizers: Toru Namerikawa (Keio University), Masaaki Nagahara (The University of Kitakyushu), Takeshi Hatanaka (Osaka University)

Time and Location: Full day, Dec. 10, 8:30-17:30 - Méditerranée 5

Abstract: Many nations are promoting projects to realize smart society through tight intertwinement between cyber and real-physical components. To this end, the framework of Cyber-Physical Systems (CPS) has successfully enabled multidisciplinary research that involves control systems, communications, networking, sensing and computing to develop new theoretical foundations/tools as well as major technological applications, including transportation, aerospace, health and medicine, robotics, manufacturing, energy management, and environment and sustainability. Construction of smart society requires not only to design these individual smart systems but also to coordinate these systems in a stable, optimal, and economically enabled fashion. A goal of this workshop is to discuss how the global perspective inherent in systems and control could contribute to designing such smart systems.
Another main issue of this workshop is how to design Cyber-Physical & Human Systems (CPHS). In smart society, human factors must be naturally involved in the overall system and they must interact with the CPS in various ways at various levels. It is thus evident that the ultimate societal outcomes of future CPHS technologies will depend crucially on deeper understanding of the interactions between cyber-physical systems and humans, and on how to integrate the human factors and their models into the CPS design in order to bring the best outcomes for individuals, organizations, and the society. Revolutionary advances in data science, machine learning, and artificial intelligence technology have opened up new possibilities of rigorously analyzing/modeling humans, not necessarily obeying any physical law, under interaction with CPS. We believe that now is an opportune time to discuss how to best consider human factors in the control loop. This workshop presents state-of-the-art research outcomes on CPHS in some key application fields including intelligent transportation, aerospace systems and robotics.

For more information, and list of speakers, please see workshop website: http://is.eei.eng.osaka-u.ac.jp/hatanaka/CDC/index.php

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Key dates (2019)
Submission Site Open:January 4
Initial Paper Submissions
to L-CSS with CDC Option Due:
March 1
Invited Session
Proposals Due:
March 7
Initial Paper
Submissions Due:
March 17
Tutorial Session
Proposals Due:
March 31
Workshop Proposals Due:May 2
Paper and Workshop
Decision Notification:
mid-July
Final Submission Open:August 1
Registration Opens:August 1
Best Student Paper
Nominations Opens:
August 1
Best Student Paper
Nominations Closes:
August 15
Accepted Papers Due:September 12
Early Bird Closes:October 1
Conference opens:December 11
Conference closes:December 13


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