Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay

被引：68

作者：

Wang, Huanqing ^{[1
,2
]}

Chen, Bing ^{[1
]}

Lin, Chong ^{[1
]}

机构：

[1] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Shandong, Peoples R China

[2] Bohai Univ, Sch Math & Phys, Jinzhou 121000, Liaoning, Peoples R China

来源：

NEUROCOMPUTING | 2012年 / 77卷 / 01期

关键词：

Adaptive control; Neural network; Backstepping; Stochastic nonlinear time-delay systems; DECENTRALIZED STABILIZATION; H-INFINITY; STABILITY; TRACKING; DESIGN;

D O I：

10.1016/j.neucom.2011.08.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov-Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：267 / 274

页数：8

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