内容简介：

In this talk, the distributed filtering problem over cyber-physical systems (CPS) is investigated. First, a scalable and fully distributed Kalman filter is provided and analyzed to achieve the consistent estimation for the states of potentially unstable systems. Then, we study the case with state equality constraints (SEC). We propose a distributed Kalman filter with guaranteed consistency and satisfied SEC. Then, we establish the relationship between consensus step, SEC and estimation error covariance in dynamic and steady processes, respectively. Under an extended collective observability condition based on SEC, the stability of the filter is studied. Moreover, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. We first propose a recursive distributed filter consisting of saturation-like observation update and consensus operation of state estimates. A sufficient condition is then established for the boundedness of estimation error.

报告人简介：

Xingkang He is a postdoctoral researcher in Division of Decision and Control Systems, KTH Royal Institute of Technology. He received the Ph.D. degree in Academy of Mathematics and Systems Science, Chinese Academy of Sciences at Beijing in 2018. He received the National Scholarship for Doctor in 2017 and the 2018 Excellent Graduate of Beijing. He also received the 2018 Best Paper Award for Data Driven Control and Learning Systems Conference. His research interests include filtering theory, event-based estimation and control, distributed parameter learning, and social networks.

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