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

被引：66
|
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
收藏
页码：267 / 274
页数：8
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