Abstract: 

This talk  introduces some of my recent research results on distributed filtering (or distributed state estimation) over multi-agent networks. In particular, the basic distributed filtering problem is first discussed and addressed in the time-driven communication scheme of agents. A scalable and fully distributed recursive filter is provided to achieve the estimation for the states of potentially unstable systems. Under very mild conditions, including the collective observability of the system and the connectivity of the multi-agent network, the stability of the proposed filter is analyzed. Then, to avoid the redundancy communications between agents, a novel event-triggered communication scheme is studied. Based on the event-triggered scheme, an event-triggered DKF is put forward and further analyzed theoretically. Finally, for a class of nonlinear uncertain systems over time-varying network topologies, an extended state method is utilized to study the distributed filtering problems. Under the jointly strong connectivity condition, it is shown that the boundedness of mean square estimation error is guaranteed.

Speaker's Biography:

Xingkang He received the B.S. degree in School of Mathematics from Hefei University of Technology in 2013, and the Ph.D. degree in Academy of Mathematics and Systems Science, Chinese Academy of Sciences at Beijing in 2018.

Dr. He received the National Scholarship for Doctor in 2017 and the 2018 Excellent Graduate of Beijing. He received the 2018 Best Paper Award of Data Driven Control and Learning Systems Conference. His research interests include filtering theory, distributed Kalman filter, event-based state estimation and multi-agent systems.