Event-triggered minimax state estimation with a relative entropy constraint

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 prob...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2019.108592