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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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 31; číslo 1; s. 76 - 88
Hlavní autoři: Luo, Biao, Yang, Yin, Liu, Derong, Wu, Huai-Ning
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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