Event-Triggered Optimal Nonlinear Systems Control Based on State Observer and Neural Network

doi:10.1007/s11424-022-1146-0

Journal of Systems Science and Complexity ›› 2023, Vol. 36 ›› Issue (1): 222-238.DOI: 10.1007/s11424-022-1146-0

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Event-Triggered Optimal Nonlinear Systems Control Based on State Observer and Neural Network

CHENG Songsong¹, LI Haoyun², GUO Yuchao², PAN Tianhong¹, FAN Yuan¹

1. Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;
2. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

Received:2021-05-08 Revised:2021-10-31 Online:2023-01-25 Published:2023-02-09
Supported by:
This paper was supported by the National Natural Science Foundation of China under Grant Nos. 61973002, 62103003, the Anhui Provincial Natural Science Foundation under Grant No. 2008085J32, the National Postdoctoral Program for Innovative Talents under Grant No. BX20180346, the General Financial Grant from the China Postdoctoral Science Foundation under Grant No. 2019M660834, and the Excellent Young Talents Program in Universities of Anhui Province under Grant No. gxyq2019002.

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CHENG Songsong, LI Haoyun, GUO Yuchao, PAN Tianhong, FAN Yuan. Event-Triggered Optimal Nonlinear Systems Control Based on State Observer and Neural Network[J]. Journal of Systems Science and Complexity, 2023, 36(1): 222-238.

This paper develops a novel event-triggered optimal control approach based on state observer and neural network (NN) for nonlinear continuous-time systems. Firstly, the authors propose an online algorithm with critic and actor NNs to solve the optimal control problem and provide an event-triggered method to reduce communication and computation burdens. Moreover, the authors design weight estimation for critic and actor NNs based on gradient descent method and achieve uniformly ultimate boundednesss (UUB) estimation results. Furthermore, by using bounded NN weight estimation and dead-zone operator, the authors propose a triggering condition, prove the asymptotic stability of closed-loop system from Lyapunov stability perspective, and exclude the Zeno behavior. Finally, the authors provide a numerical example to illustrate the effectiveness of the proposed method.

Key words: Event-triggered control, neural network, optimal control, state observer

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