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

• Published in: Neurocomputing (Amsterdam) Vol. 103; pp. 155 - 163
• Main Authors: Wang, Huanqing, Chen, Bing, Lin, Chong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2013; Elsevier
• Subjects: Adaptative systems; Adaptive control systems; Adaptive decentralized control; Applied sciences; Backstepping approach; Computer science; control theory; systems; Control systems; Control theory. Systems; Decentralized control; Exact sciences and technology; Fuzzy; Fuzzy logic; Fuzzy logic systems; Fuzzy set theory; Nonlinearity; Stochastic nonlinear large-scale systems; Stochasticity; Stochastic nonlinear large-scale systems; Backstepping approach; Adaptive decentralized control; Fuzzy logic systems; Closed loop; Fuzzy system; Probabilistic approach; Non linear control; Logical programming; Non linear system; Recursive algorithm; Adaptive control; Adaptive method; Stochastic system; Backstepping control; Closed feedback; Fuzzy logic; Fuzzy control; Uncertain system; Decentralized control; Efficiency; Large scale; Lyapunov function; Large scale system
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2012.09.016

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.