Event-triggered minimax state estimation with a relative entropy constraint

• Published in: Automatica (Oxford) Vol. 110; p. 108592
• Main Authors: Xu, Jiapeng, Tang, Yang, Yang, Wen, Li, Fangfei, Shi, Ling
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2019
• Subjects: Event-triggered state estimation; Minimax estimation; Relative entropy constraint; Robustness; Relative entropy constraint; Minimax estimation; Robustness; Event-triggered state estimation
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2019.108592

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.