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)
Schlagworte:	Adaptive control Adaptive dynamic programming (ADP) Artificial neural networks Computer simulation Dynamic programming event-triggered Indexes Learning systems Lower bounds Mathematical model neural network (NN) Neural networks Nonlinear control Nonlinear systems Optimal control Performance analysis performance guarantee Upper bounds
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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