Adaptive NN tracking control with prespecified accuracy for a class of uncertain periodically time-varying and nonlinearly parameterized switching systems

被引：4

作者：

Yang, Xiaoli ^{[1
]}

Li, Jing ^{[1
]}

Zhang, Zhaohui ^{[1
]}

机构：

[1] Xidian Univ, Sch Math & Stat, Xian 710071, Peoples R China

来源：

NEUROCOMPUTING | 2021年 / 435卷

基金：

中国国家自然科学基金;

关键词：

Prespecified accuracy; Neural networks; Periodically time-varying parameters; Arbitrary switching system; Nonlinear parameterization; DYNAMIC SURFACE CONTROL; STRICT-FEEDBACK SYSTEMS; NEURAL-NETWORK CONTROL; UNIVERSAL APPROXIMATION; BACKSTEPPING CONTROL; LYAPUNOV FUNCTIONS; LEARNING CONTROL; FUZZY CONTROL; DESIGN; STABILIZATION;

D O I：

10.1016/j.neucom.2021.01.017

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 focuses on a tracking control issue for a class of uncertain switching nonlinear systems under arbitrary switching with unknown time-varying parameters. An approximator is designed by combining Multilayer neural network (MNN) with Fourier series expansion (FSE) to approximate the nonlinearly parameterized unknown functions. Then, a common Lyapunov function (CLF) is constructed and a novel adaptive neural network (NN) control scheme is proposed by using the adaptive Backstepping technique. It can be proven that the tracking error falls into a prespecified domain of equilibrium and the whole system is semi-globally uniformly ultimately bounded (SGUUB). The effectiveness of the designed approach is verified through two simulation examples. (c) 2021 Elsevier B.V. All rights reserved.

引用

页码：273 / 282

页数：10

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