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RESEARCH PRODUCT

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

Huanqing WangQi ZhouXuebo YangHamid Reza Karimi

subject

Engineering (all)Article Subjectlcsh:TA1-2040lcsh:MathematicsVDP::Technology: 500::Mechanical engineering: 570Mathematics (all)lcsh:Engineering (General). Civil engineering (General)lcsh:QA1-939Mathematics (all); Engineering (all)

description

Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/841306 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. © 2014 Huanqing Wang et al.

yearjournalcountryeditionlanguage
2014-01-01Mathematical Problems in Engineering
10.1155/2014/841306http://dx.doi.org/10.1155/2014/841306