Local Tracking Control for Unknown Interconnected Systems via Neuro-Dynamic Programming

被引：0

作者：

Zhao, Bo ^{[1
]}

Liu, Derong ^{[2
]}

Ha, Mingming ^{[3
]}

Wang, Ding ^{[1
]}

Xu, Yancai ^{[1
]}

Wei, Qinglai ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China

[3] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2018), PT VII | 2018年 / 11307卷

基金：

中国国家自然科学基金;

关键词：

Adaptive dynamic programming; Neuro-dynamic programming; Local tracking control; Optimal control; Unknown interconnected systems; ZERO-SUM GAMES;

D O I：

10.1007/978-3-030-04239-4_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper develops a neuro-dynamic programming based local tracking control (LTC) scheme for unknown interconnected systems. By using the local input-output data and the desired states of coupling subsystems, a local neural network (NN) identifier is established to obtain the local input gain matrix online. By introducing a modified local cost function, the Hamilton-Jacobi-Bellman equation is solved by a local critic NN with asymptotically convergent weight vector, which is obtained by nested update law, and the LTC can be derived with the desired state aided augmented subsystem. The stability of the closed-loop system is shown by Lyapunov's direct method. The simulation on the parallel inverted pendulum system illustrates that the developed LTC scheme is effective.

引用

页码：258 / 268

页数：11

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