Adaptive neural control for nonlinear systems with actuator faults and unknown control directions via command filter

被引：10

作者：

Guo, Jun ^{[1
,2
]}

Bo, Yuming ^{[1
]}

Park, Ju H. ^{[2
]}

Ma, Jiali ^{[3
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China

[2] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea

[3] Southeast Univ, Sch Automat, Nanjing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2022年 / 32卷 / 04期

基金：

中国博士后科学基金; 新加坡国家研究基金会;

关键词：

adaptive neural network control; command filter; unknown actuator faults; unknown control directions; OUTPUT-FEEDBACK CONTROL; MULTIAGENT SYSTEMS;

D O I：

10.1002/rnc.5929

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article studies the issue of command filter-based adaptive fault-tolerant control for a class of nonlinear systems subject to unknown control directions and disturbance. First, the neural network is employed to deal with the nonlinear functions, and the explosion of the complexity problem is handled by the command filter approach. Second, the bound estimation method and the Nussbaum function are utilized to compensate for the influence of the actuator faults and the unknown directions, respectively. Finally, the tracking error signals are guaranteed to converge into bounded compact sets around the origin, and all closed-loop signals are bounded. The effectiveness of the proposed method is illustrated by three simulations.

引用

页码：2100 / 2118

页数：19

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