Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.automatica.2019.108592 | Automatica |
Keywords | Field | DocType |
Event-triggered state estimation,Minimax estimation,Robustness,Relative entropy constraint | Dynamic programming,Mathematical optimization,Minimax,Minimax estimator,Probability measure,Quadratic equation,Gaussian,Kullback–Leibler divergence,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
110 | 1 | 0005-1098 |
Citations | PageRank | References |
3 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiapeng Xu | 1 | 3 | 0.36 |
| Yang Tang | 2 | 392 | 21.87 |
| Wen Yang | 3 | 95 | 10.06 |
| Fangfei Li | 4 | 361 | 22.25 |
| Ling Shi | 5 | 1717 | 107.86 |