Adaptive neural control for a general class of pure-feedback stochastic nonlinear systems

被引：24
|
作者：
Wang, Huanqing [1 ]
Liu, Xiaoping [2 ,4 ]
Liu, Kefu [2 ]
Chen, Bing [3 ]
Lin, Chong [3 ]
机构：
[1] Bohai Univ, Sch Math & Phys, Jinzhou 121000, Liaoning, Peoples R China
[2] Lakehead Univ, Fac Engn, Thunder Bay, ON P7B 5E1, Canada
[3] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Shandong, Peoples R China
[4] Liaoning Univ Sci & Technol, Fac Elect & Informat Engn, Anshan 114051, Liaoning, Peoples R China
来源：
NEUROCOMPUTING | 2014年 / 135卷
关键词：
Adaptive neural control; Stochastic pure-feedback nonlinear systems; Backstepping; DYNAMIC SURFACE CONTROL; FUZZY-TRACKING CONTROL; UNKNOWN DEAD-ZONE; INPUT CONSTRAINTS; NETWORK CONTROL; DESIGN; STABILIZATION; DELAY; FORM;
D O I：
10.1016/j.neucom.2013.12.030
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 the adaptive neural control is considered for a class of pure-feedback stochastic nonlinear systems. Based on the radial basis function (RBF) neural networks' universal approximation property, an adaptive neural controller is developed via backstepping technique. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are bounded in the sense of mean quartic value. Compared with the existing results on adaptive control of stochastic pure-feedback nonlinear systems, the main novelty of this note is that a systematic design procedure is presented for a class of pure-feedback stochastic nonlinear systems with a more general form of the diffusion term. Simulation results are presented to demonstrate the effectiveness of the proposed scheme. (C) 2014 Elsevier B.V. All rights
引用
收藏
页码：348 / 356
页数：9
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