Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones | Zendy

Huanqing Wang | Zendy; Qi Zhou | Zendy; Xuebo Yang | Zendy; Hamid Reza Karimi | Zendy

Open Access

Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

Author(s) -

Huanqing Wang,

Qi Zhou,

Xuebo Yang,

Hamid Reza Karimi

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/841306

Subject(s) - control theory (sociology) , nonlinear system , dead zone , bounded function , controller (irrigation) , artificial neural network , class (philosophy) , scheme (mathematics) , computer science , uniform boundedness , adaptive control , scale (ratio) , decentralised system , control (management) , mathematics , artificial intelligence , mathematical analysis , oceanography , physics , quantum mechanics , agronomy , biology , geology

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

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