Plenary Speech 4

Plenary  Speech 4 : Advanced Model Predictive Control (MPC) Framework for Autonomous Intelligent Systems

Yang Shi (IEEE Fellow) received his B.Sc. and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 1994 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005. From 2005 to 2009, he was an Assistant Professor and Associate Professor in the Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. In 2009, he joined the University of Victoria, and now he is a Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, and energy system applications. Dr. Shi is the recipient of the JSPS Invitation Fellowship in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018; CSME Mechatronics Medal (2023); IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2023). He is IFAC Council Member; VP on Conference Activities IEEE IES and the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Co-Editor-in-Chief of IEEE TIE, and Editor-in-Chief of IEEE Canadian Journal of Electrical and Computer Engineering; he also serves as Associate Editor for Automatica, IEEE TAC, etc. He is a Distinguished Lecturer of IES. He is a Fellow of IEEE, ASME, CSME, Engineering Institute of Canada (EIC), Canadian Academy of Engineering (CAE), and a registered Professional Engineer in British Columbia, Canada.

Abstract: Autonomous intelligent systems, which lie at the intersection of unmanned systems, robotics, systems and control, multi-agent systems, networked and distributed systems, machine learning, etc. Autonomous intelligent systems are equipped with abilities such as sensing and perception, data processing and information fusion, intelligent decision making, autonomous control, learning and adaption, communications and computation, thus can achieve a high level of autonomy to perform missions without human intervention or can naturally interact and collaborate with humans and/or environment. The fundamental control theory and methods in autonomous intelligent systems are of central importance in orchestrating all related functions. Autonomous control and intelligence can be applied to various systems, e.g., aerial vehicles, marine vehicles, ground robots, space exploration, energy and power systems, transportation and smart city, intelligent agriculture, smart manufacturing, smart health care systems, Internet of Things, etc. Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of autonomous intelligent systems. This talk will firstly summarize the major application requirements and challenges to innovate in designing, implementing, deploying and operating autonomous intelligent systems. Further, the robust resilient MPC and distributed MPC design framework will be presented. Finally, the application of MPC algorithms to various autonomous intelligent systems will be illustrated.