Event-Triggered Optimal Control With Performance Guarantees Using Adaptive Dynamic Programming

This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performan...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 31; H. 1; S. 76 - 88
Hauptverfasser: Luo, Biao, Yang, Yin, Liu, Derong, Wu, Huai-Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2899594