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Original Article

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

Tong ZhaoContact Information

(1)  Department of Automatic Control, Qingdao University of Science and Technology, Qingdao, China

Received: 31 January 2007  Accepted: 24 April 2007  Published online: 22 May 2007

Abstract  For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of non-affine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.

Keywords  Decentralized control - Adaptive control - RBFN - Non-affine - Large-scale nonlinear system


Contact Information Tong Zhao
Email: zhaotong.qd@163.com
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Referenced by
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  1. Han, Honggui (2010) Research on an online self-organizing radial basis function neural network. Neural Computing and Applications
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