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

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

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 package...

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Main Authors: Huanqing Wang, Qi Zhou, Xuebo Yang, Hamid Reza Karimi
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/841306
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spelling doaj-1e2d969d6b1046e79951151b933878682020-11-24T22:44:54ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/841306841306Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead ZonesHuanqing Wang0Qi Zhou1Xuebo Yang2Hamid Reza Karimi3School of Mathematics and Physics, Bohai University, Liaoning, Jinzhou 121000, ChinaCollege of Information Science and Technology, Bohai University, Liaoning, Jinzhou 121000, ChinaCollege of Engineering, Bohai University, Liaoning, Jinzhou 121000, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayThe 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.http://dx.doi.org/10.1155/2014/841306
collection DOAJ
language English
format Article
sources DOAJ
author Huanqing Wang
Qi Zhou
Xuebo Yang
Hamid Reza Karimi
spellingShingle Huanqing Wang
Qi Zhou
Xuebo Yang
Hamid Reza Karimi
Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
Mathematical Problems in Engineering
author_facet Huanqing Wang
Qi Zhou
Xuebo Yang
Hamid Reza Karimi
author_sort Huanqing Wang
title Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
title_short Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
title_full Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
title_fullStr Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
title_full_unstemmed Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones
title_sort robust decentralized adaptive neural control for a class of nonaffine nonlinear large-scale systems with unknown dead zones
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/841306
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AT qizhou robustdecentralizedadaptiveneuralcontrolforaclassofnonaffinenonlinearlargescalesystemswithunknowndeadzones
AT xueboyang robustdecentralizedadaptiveneuralcontrolforaclassofnonaffinenonlinearlargescalesystemswithunknowndeadzones
AT hamidrezakarimi robustdecentralizedadaptiveneuralcontrolforaclassofnonaffinenonlinearlargescalesystemswithunknowndeadzones
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