Event-triggered minimax state estimation with a relative entropy constraint. (December 2019)

• Record Type: Journal Article
• Authors: Xu, Jiapeng; Tang, Yang; Yang, Wen; Li, Fangfei; Shi, Ling
• Is Part Of: Automatica. Volume 110(2019)
• Publication Date: 2019-12
• Subjects: Event-triggered state estimation -- Minimax estimation -- Robustness -- Relative entropy constraint; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.108592
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In this paper, we consider an event-triggered minimax state estimation problem for uncertain systems subject to a relative entropy constraint. This minimax estimation problem is formulated as an equivalent event-triggered linear exponential quadratic Gaussian problem. It is then shown that this problem can be solved via dynamic programming and a newly defined information state. As the solution to this dynamic programming problem is computationally intractable, a one-step event-triggered minimax estimation problem is further formulated and solved, where an a posteriori relative entropy is introduced as a measure of the discrepancy between probability measures. The resulting estimator is shown to evolve in recursive closed-form expressions. For the multi-sensor system scenario, a one-step event-triggered minimax estimator is also presented in a sequential fusion way. Finally, comparative simulation examples are provided to illustrate the performance of the proposed one-step event-triggered minimax estimators.