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

  • Authors:
  • Huanqing Wang;Bing Chen;Chong Lin

  • Affiliations:
  • Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong, PR China and School of Mathematics and Physics, Bohai University, Jinzhou 121000, Liaoning, PR China;Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong, PR China;Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong, PR China

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

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.