The 39th Chinese Control Conference
System Performance Analysis and Optimization
Speaker: Zhendong Sun
Affiliation: Key Laboratory of Systems & Control, Academy of Mathematics & Systems Science, Chinese Academy of Sciences (CAS), China
Title: Stabilizing Design of Switched Linear Systems
Abstract: In this talk, we focus on the problem of stabilization for switched linear systems. While much progress has been made, the problem is still largely open. We review the essential difficulties for solving the problem, including 1) non-existence of convex control-Lyapunov functions; 2) the one-subsystem-one-linear-feedback scheme is generally insufficient; and 3) finite-automaton-induced switching is generally insufficient. We introduce recent progress towards (partly) overcoming the difficulties.
Biography: Zhendong Sun is with the Key Laboratory of Systems & Control, Academy of Mathematics & Systems Science, Chinese Academy of Sciences, where he is currently a Researcher. His research interests are in the fields of nonlinear control systems, switched and hybrid systems, and nano-micro-electronic systems. He is the first author of the monographs ``Switched Linear Systems-Control and Design'' and ``Stability Theory of Switched Dynamical Systems" (London: Springer, 2005 & 2011). He serves/served as Associate Editor for IEEE Transactions on Automatic Control and International Journal of Robust and Nonlinear Control.
Speaker: Xiangru Xu
Affiliation: Department of Mechanical Engineering in the University of Wisconsin-Madison, USA
Title:Control Barrier Functions for Safety-Critical Control of Dynamic Systems
Abstract: We are witnessing a new era of autonomy – from self-driving cars in the streets, to delivery drones in the sky, to exploration rovers on Mars. These autonomous systems are safety critical and involve the tight coupling between potentially conflicting control objectives and safety constraints. In this talk, I will describe a provably-safe optimization-based feedback control framework for the safety-critical control of dynamic systems where the safety condition is specified in terms of forward invariance of a set and verified via control barrier functions and the control objective is specified in terms of control Lyapunov functions. I will also demonstrate the application of this framework to robotic systems.
Biography: Xiangru Xu is currently an assistant professor in the Department of Mechanical Engineering in the University of Wisconsin-Madison, USA. Before joining UW-Madison, he held postdoc positions in the University of Michigan-Ann Arbor and the University of Washington-Seattle. He received his Ph.D. degree from Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and B.S. degree from Beijing Normal University. He received the Best New Application Paper Award from IEEE Transactions on Automation Science and Engineering in 2019.
Speaker: Zhongsheng Hou
Affiliation: School of Automation, Qingdao University, Qingdao, China
Title:Does R. E. Kalman’s Paradigm Still Works at the Big Data/AI Age?
Abstract: Professor R. E. Kalman was the founder and visionary intellectual leader of the field of mathematical system theory. His contributions to optimal control, optimal estimation and filtering, realization theory, and mathematical system theory are at the foundations of these fields. They have significantly influenced much of the subsequent developments. Their influence transcends well beyond system and control into diverse fields of engineering, mathematics, physical sciences, social sciences, and others. However, there have been very significant developments in science, engineering, technology, and society in the last few decades. It is clear that change will accelerate further in the coming decades. Thus, thinking about the relevance and implications of the Kalman’s paradigm of the control theory under the big data or the AI age, that might illuminate the path of the system and control research for the future.
This talk includes five parts. What is the Kalman’s Paradigm; The Challenges under the Kalman’s Paradigm the Problems We Face under the Big Data/AI Age; The Possible Solutions for the Post-Kalman Era; and the Conclusion.
Biography: Zhongsheng Hou (SM’13) received the B.S. and M.S. degrees from Jilin University of Technology, Jilin, China, in 1983 and 1988, respectively, and the Ph.D. degree from Northeastern University, Shenyang, China, in 1994.
From 1995 to 1997, he was a Postdoctoral Fellow with Harbin Institute of Technology, Harbin, China. From 2002 to 2003, he was a Visiting Scholar with Yale University, CT, USA. From 1997 to 2018, he was with Beijing Jiaotong University, Beijing, China, where he was a Distinguished Professor and the Founding Director of Advanced Control Systems Lab, and the Head of the Department of Automatic Control. He is currently a Chair Professor with the School of Automation, Qingdao University, Qingdao, China.
His research interests are in the fields of data-driven control, model-free adaptive control, learning control, and intelligent transportation systems. Up to now, he has authored or co-authored more than 180 peer-reviewed journal papers and over 140 papers in prestigious conference proceedings. He has authored two monographs, Nonparametric Model and its Adaptive Control Theory, Science Press (in Chinese), 1999, and Model Free Adaptive Control: Theory and Applications, CRC Press, 2013. His pioneering work on model-free adaptive control has been verified in more than 160 different field applications, laboratory equipment and simulations with practical background, including wide-area power systems, lateral control of autonomous vehicles, temperature control of silicon rod. His works on data-driven learning and control has been supported by multiple projects supported by the National Natural Science Foundation of China (NSFC), including three Key Projects in 2009, 2015, and 2019, respectively, and a Major International Cooperation Project in 2012.
Prof. Hou is the Founding Director of the Technical Committee on Data Driven Control, Learning and Optimization (DDCLO), Chinese Association of Automation (CAA), and is a Fellow of CAA. He is also an International Federation of Automatic Control Technical Committee Member of both “Adaptive and Learning Systems” and “Transportation Systems.” Dr. Hou was the Guest Editor for two Special Sections on the topic of data-driven control of the IEEE TRANSACTIONS ON NEURAL NETWORKS in 2011, and the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS in 2017.
Speaker: Dongbin Zhao
Affiliation: Institute of Automation, Chinese Academy of Sciences, and also with the University of Chinese Academy of Sciences, China
Title:Deep reinforcement learning algorithms and applications
Abstract: Deep reinforcement learning (DRL), combines the merits of the decision ability of reinforcement learning and the perception ability of deep learning, is becoming a major artificial intelligence (AI) algorithm. Recently, Google DeepMind proposed several DRL algorithms to conquer the Atari video games, Go, and Starcraft II, thought to be several milestones in AI. The corresponding papers were also published in Nature and Science. Other teams in OpenAI and Microsoft also had great achievements in Dota2 video game and Mahjong separately with the strong support of DRL algorithm. This talk will introduce these shining hotspots, typical DRL algorithms, related applications, and discuss future potential directions.
Biography: Dongbin Zhao is a professor at Institute of Automation, Chinese Academy of Sciences, and also with the University of Chinese Academy of Sciences, China. He has published 4 books, and over 80 international journal papers. Dr. Zhao is an IEEE senior member. He serves as the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Cybernetics, IEEE Computation Intelligence Magazine (CIM), etc. He is the Chair of Technical Activities Strategic Planning Sub-Committee, and was the Chair of Beijing Chapter, Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society. He works as several guest editors of renowned international journals, including two lead guest editors on deep reinforcement learning in IEEE TNNLS and IEEE CIM. He is involved in organizing many international conferences. His current research interests are in the area of deep reinforcement learning, computational intelligence, autonomous driving, game artificial intelligence, robotics, smart grids, etc.
Affiliation:School of Electrical Engineering and Computing, the University of Newcastle, Australia
Title:Secure Networked Control of Large-Scale Cyberphysical Systems using Cryptographic Techniques
Abstract: This work aims to create a secure environment for networked control systems composed of multiple dynamic entities and computational control units via networking, in the presence of disclosure attacks. In particular, we consider the situation where some dynamic entities or control units are vulnerable to attacks and can become malicious. Our objective is to ensure that the input and output data of the benign entities are protected from the malicious entities as well as protected when they are transferred over the networks in a distributed environment. Both these security requirements are achieved using cryptographic techniques. However, the use of cryptographic mechanisms brings additional challenges to the design of controllers in the encrypted state space; the closed-loop system gains and states are required to match the specified cryptographic algorithms. We propose a methodology for the design of secure networked control systems integrating the cryptographic mechanisms with the control algorithms. The approach is based on the separation principle, with the cryptographic techniques addressing the security requirements and the control algorithms satisfying their performance requirements.
Biography: Zhiyong Chen received his Bachelor degree from the Department of Automation, University of Science and Technology of China in 2000. He received his M.Phil. and Ph.D. degrees from the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, in 2002 and 2005, respectively. He worked as a Research Associate at the University of Virginia during 2005-2006. He joined the School of Electrical Engineering and Computing, the University of Newcastle, Australia in 2006 where he is currently a Full Professor. He was elected to Changjiang Scholar Chair Professorship with Central South University in 2016. He is the author of over 100 journal papers and one textbook. He is the recipient of National Natural Science Award of China (second prize) in 2010, and several international conference best paper awards. He was/is an Associate Editor for many international journals including IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Cybernetics.
Affiliation: Department of Automation, Tsinghua University, China
Title: Secure Information Fusion in Cyber-Physical Systems
Abstract: The concept of Cyber-Physical System (CPS) refers to the embedding of sensing, communication, control and computation into the physical spaces. Today, CPSs can be found in areas as diverse as aerospace, automotive, chemical process control, civil infrastructure, energy, health-care, manufacturing and transportation, most of which are safety critical. Any successful attack to such kind of systems can cause major disruptions, leading to great economic losses and may even endanger human lives. The first-ever CPS malware (called Stuxnet) was found in July 2010 and has raised significant concerns about CPS security. In this talk we discuss how to design secure and efficient information fusion algorithms for CPS. We first consider the binary hypothesis testing problem with multiple sensors and design secure algorithm against an unknown set of Byzantine sensors. We further quantify the cost of adding security to the system and prove that our algorithm causes minimum impact on the performance in the absence of an attack. Next we consider the state estimation problem, and prove necessary and sufficient conditions, under which a convex optimization based estimator is secure against Byzantine attacks.
Biography: Yilin Mo is an Associate Professor in the Department of Automation, Tsinghua University. He received his Ph.D. In Electrical and Computer Engineering from Carnegie Mellon University in 2012 and his Bachelor of Engineering degree from Department of Automation, Tsinghua University in 2007. Prior to his current position, he was a postdoctoral scholar at Carnegie Mellon University in 2013 and California Institute of Technology from 2013 to 2015. He held an assistant professor position in the School of Electrical and Electronic Engineering at Nanyang Technological University from 2015 to 2018. His research interests include secure control systems and networked control systems, with applications in autonomous driving and sensor networks.