Adaptive fuzzy decentralized control for a class of large-scale stochastic nonlinear systems

被引：45

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 | 2013年 / 103卷

关键词：

Stochastic nonlinear large-scale systems; Fuzzy logic systems; Adaptive decentralized control; Backstepping approach; OUTPUT-FEEDBACK CONTROL; NEURAL-CONTROL; TRACKING CONTROL; STABILIZATION; DESIGN; APPROXIMATION; IMPROVEMENT;

D O I：

10.1016/j.neucom.2012.09.016

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, an adaptive fuzzy decentralized control approach is proposed for a class of uncertain stochastic nonlinear large-scale systems. Fuzzy logic systems are used to approximate the unknown nonlinearities and backstepping technique is utilized to construct adaptive fuzzy decentralized controller. It is shown that the proposed control scheme guarantees that all the closed-loop systems are semi-globally uniformly ultimately bounded in probability. Compared with the existing adaptive fuzzy decentralized control approaches, the proposed controller is simpler, and only one adaptive parameter needs to be estimated online for each subsystem. A numerical example is provided to illustrate the effectiveness of the suggested approach. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：155 / 163

页数：9

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