讲座题目：Advanced Model Predictive Control (MPC) Framework for Autonomous Intelligent Systems
讲座时间：2022/08/19（周五）上午9:00-11:00
腾讯会议ID：270-852-620
会议链接：https://meeting.tencent.com/dm/RNCL3sIwz98Z
直播地址：https://meeting.tencent.com/l/mJOKwWE45cYZ
邀请人：雷亚国教授
讲座人：Yang Shi教授
施阳博士，1998年于西北工业大学获得博士学位；2005年于加拿大阿尔伯塔大学获得电子与计算机工程博士学位。2005-2009在加拿大萨斯喀彻温大学任助理教授及副教授；目前为加拿大维多利亚大学机械工程系终身教授。施阳博士的研究集中于工业信息物理系统、网络及分布式控制系统、模型预测控制、机电系统及机器人系统设计与控制、能源系统的优化调度与控制等。
他2007获得加拿大萨斯喀彻温大学十佳教学奖；2012年获得维多利亚大学工学院最佳教学奖。2015年获得维多利亚大学年度唯一的最佳研究银奖(Craigdarroch Silver Medal)；2013年获得日本学术振兴会特邀访问教授奖；2017年获得德国洪堡研究基金。他与学生合著的论文获得2017年IEEE Transactions on Fuzzy Systems年度最佳论文奖。目前任IEEE工业电子学会Vice President,工业信息物理系统技术委员会主席，担任IEEE Transactions on Industrial Electronics的共同主编(Co-Editor-in-Chief)，任多家国际期刊的副编辑，包括：Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics等。
施阳教授是加拿大工程研究院Fellow (Fellow of Engineering Institute of Canada)，是IEEE Fellow(国际电子电气工程师协会), ASME Fellow(美国机械工程师协会)和CSME Fellow(加拿大机械工程师协会)。
Biography: Yang SHIreceived 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, Saskatchewan, 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, British Columbia, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications.
Dr. Shi received the University of Saskatchewan Student Union Teaching Excellence Award in 2007. At the University of Victoria, he received the Faculty of Engineering Teaching Excellence in 2012, and the Craigdarroch Silver Medal for Excellence in Research in 2015. He received the JSPS Invitation Fellowship (short-term), and was a Visiting Professor at the University of Tokyo during Nov-Dec 2013. His co-authored paper was awarded the 2017IEEE Transactions on Fuzzy SystemsOutstanding Paper Award. He received the Humboldt Research Fellowship for Experienced Researchers in 2018. He is a member of the IEEE IES Administrative Committee during 2017-2019, and the founding Vice Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Co-Editor-in-Chief forIEEE Transactions on Industrial Electronics; he also serves as Associate Editor forAutomatica,IEEE Trans. Automatic Control,IEEE Trans. Cybernetics, etc. He is a Fellow of IEEE, ASME and CSME, and a registered Professional Engineer in British Columbia, Canada. Heis a Fellow of Engineering Institute of Canada (EIC).
讲座简介：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 MPC and distributed MPC design framework will be presented. Finally, the application of MPC algorithms to various autonomous intelligent systems will be illustrated.
