Adaptive decentralized NN control of large-scale stochastic nonlinear time-delay systems with unknown dead-zone inputs

被引：38

作者：

Cui, Guozeng ^{[1
]}

Wang, Zhen ^{[2
]}

Zhuang, Guangming ^{[1
,3
]}

Li, Ze ^{[4
]}

Chu, Yuming ^{[5
]}

机构：

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

[2] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 265590, Shandong, Peoples R China

[3] Liaocheng Univ, Sch Math Sci, Liaocheng 252059, Shandong, Peoples R China

[4] Suzhou Univ Sci & Technol, Sch Mech & Elect Engn, Suzhou 215000, Jiangsu, Peoples R China

[5] Huzhou Teachers Coll, Sch Sci, Huzhou 313000, Zhejiang, Peoples R China

来源：

NEUROCOMPUTING | 2015年 / 158卷

基金：

中国国家自然科学基金;

关键词：

Adaptive decentralized control; Stochastic nonlinear systems; Neural network (NN); Time-varying delays; Dead-zones; DYNAMIC SURFACE CONTROL; OUTPUT-FEEDBACK CONTROL; NEURAL-CONTROL; VARYING DELAY; TRACKING; STABILIZATION;

D O I：

10.1016/j.neucom.2015.01.048

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, the problem of adaptive decentralized neural network (NN) control for a class of large-scale stochastic nonlinear time-delay systems with unknown dead-zone inputs is investigated. Neural networks are utilized to approximate unknown nonlinear functions, and an adaptive decentralized controller is constructed by incorporating the minimal learning parameters algorithm into backstepping design procedure. It is proved that the proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability. Finally, a numerical example is provided to demonstrate the effectiveness of the present results. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：194 / 203

页数：10

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