摘要：This talk presents the properties of the exact and approximate set-valued Kalman filters with an emphasis on event-based state estimation. The properties investigated, including 1) the independence of the set-valued filters on the sensor fusion sequences at the same time instant, 2) the asymptotic boundedness of the exact and approximate sets of estimation means and 3) the possibility of performance improvement by introducing additional set-valued sensor measurements, guarantee the worst-case performance of the set-valued event-based estimators. Based on these properties, an event-triggering condition design method considering the requirements on estimation performance and communication rates is introduced. 

个人简介：Dawei Shi received the B.Eng. degree in Electrical Engineering and Automation from the Beijing Institute of Technology in 2008 and the Ph.D. degree in Control Systems from the University of Alberta in 2014. Since September 2014, he has been appointed as an Associate Professor in the School of Automation at the Beijing Institute of Technology, Beijing, China.

His research interests include event-based control and estimation, robust model predictive control and tuning, and wireless sensor networks. He was the recipient of the Best Student Paper Award in the 2009 IEEE International Conference on Automation and Logistics.