Event-triggered control for input constrained non-affine nonlinear systems based on neuro-dynamic programming

被引：14
作者：
Zhang, Shunchao [1 ]
Zhao, Bo [2 ]
Zhang, Yongwei [1 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
[2] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China
来源：
NEUROCOMPUTING | 2021年 / 440卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Neuro-dynamic programming; Adaptive dynamic programming; Event-triggered control; Neural networks; Non-affine nonlinear systems; Input constraints; H-INFINITY CONTROL; ROBUST-CONTROL; ALGORITHMS;
D O I：
10.1016/j.neucom.2021.01.116
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a neuro-dynamic programming (NDP)-based event-triggered control (ETC) method is proposed for unknown non-affine nonlinear systems with input constraints. A neural network-based identifier is established with measurable input and output data to learn the unknown system dynamics. Then, a critic neural network is employed to approximate the value function for solving the event triggered Hamilton-Jacobi-Bellman equation. Furthermore, an NDP-based ETC scheme is developed, which samples the states and updates the control law when the triggering condition is violated. Compared with the traditional time-triggered control methods, the ETC method can reduce computational burden, communication cost and bandwidth. In addition, the stability of the closed-loop system and the weight error convergence of the critic neural network are provided based on the Lyapunov?s direct method. The intersamling time is proved to be bounded by a positive constant, which excludes the Zeno behavior. Finally, two case studies are provided to verify the effectiveness of the developed ETC method. CO 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码：175 / 184
页数：10
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