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

• Published in: Journal of systems science and complexity Vol. 36; no. 1; pp. 222 - 238
• Main Authors: Cheng, Songsong, Li, Haoyun, Guo, Yuchao, Pan, Tianhong, Fan, Yuan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023; Springer Nature B.V
• Subjects: Algorithms; Closed loops; Complex Systems; Continuous time systems; Control; Feedback control; Mathematics; Mathematics and Statistics; Mathematics of Computing; Neural networks; Nonlinear control; Nonlinear systems; Operations Research/Decision Theory; Optimal control; Stability; State observers; Statistics; Systems Theory; optimal control; Event-triggered control; state observer; neural network
• ISSN: 1009-6124; 1559-7067
• DOI: 10.1007/s11424-022-1146-0

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.