Neural Network Direct Adaptive Control Strategy for a Class of Switched Nonlinear Systems

被引：3

作者：

Yu, Lei ^{[1
,2
]}

Jiang, Xiefu ^{[1
]}

Fei, Shumin ^{[3
]}

Huang, Jun ^{[2
]}

Yang, Gang ^{[4
]}

Qian, Wei ^{[5
]}

机构：

[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China

[2] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China

[3] Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Jiangsu, Peoples R China

[4] Digital Mfg Technol Key Lab Jiangsu Prov, Huaian 223003, Peoples R China

[5] Henan Prov Open Lab Control Engn Key Discipline, Jiaozuo 454000, Peoples R China

来源：

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2016年 / 138卷 / 08期

基金：

中国国家自然科学基金;

关键词：

switched nonlinear systems; neural network direct adaptive control; RBF neural networks; switching signal; average dwell-time method; TRACKING CONTROL; NN CONTROL; STABILIZATION; DESIGN;

D O I：

10.1115/1.4033485

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 paper deals with the adaptive neural network (NN) switching control problem for a class of switched nonlinear systems. Radial basis function (RBF) NNs are utilized to approximate the unknown switching control law term which includes a neural network control term, a supervisory control term, and a compensation control term. Also, based on the average dwell-time, a direct adaptive neural switching controller is designed to heighten the robustness of switching system. We can prove to ensure stability of the resulting closed-loop system such that the output tracking performance can be well obtained and all the signals are kept bounded. Simulation results validate the tracking control performance and investigate the effectiveness of the proposed switching control method.

引用

页数：7

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