ECE Seminar - Event-triggered learning for data-driven predictive control
Abstract: Advancements in new sensing devices and big data technology have enabled the generation and collection of massive input-output data for control system design. Under this background, it becomes very important to evaluate the validity and effectiveness of the data sampled to be used in modeling and control. In this work, we introduce an event-triggered learning scheme that can evaluate the importance of incoming data samples online and only update model learning when informative data samples are identified. We also show the possibility of applying the learning approach to data-driven predictive control, through tube-based/minmax MPC approaches.
Dawei Shi received the B.Eng. degree in electrical engineering and its automation from the Beijing Institute of Technology, Beijing, China, in 2008, the Ph.D. degree in control systems from the University of Alberta, Edmonton, AB, Canada, in 2014. In December 2014, he was appointed as an Associate Professor at the School of Automation, Beijing Institute of Technology. From February 2017 to July 2018, he was with the Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, as a Postdoctoral Fellow in bioengineering. Since July 2018, he has been with the School of Automation, Beijing Institute of Technology, where he is a professor. His research focuses on the analysis and synthesis of complex sampled data control systems with applications to biomedical engineering, robotics, and motion systems. He serves as an Associate Editor/Technical Editor for IEEE Transactions on Industrial Electronics, IEEE/ASME Transactions on Mechatronics, IEEE Control Systems Letters, the IFAC journal of Control Engineering Practice, and IET Control Theory and Applications. He served as an associate editor for IFAC World Congress and is a member of the IEEE Control Systems Society Conference Editorial Board. He is a Senior Member of the IEEE.