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Adaptive Self-constructing Radial-Basis-Function Neural Control for MIMO Uncertain Nonlinear Systems with Unknown Disturbances

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Indexed by:会议论文

Date of Publication:2014-07-06

Included Journals:EI、CPCI-S、Scopus

Page Number:3278-3283

Abstract:In this paper, an adaptive self-constructing RBF neural control (AS-RBFNC) scheme for trajectory tracking of MIMO uncertain nonlinear systems with unknown time-varying disturbances is proposed. System uncertainties and unknown dynamics can be exactly identified online by a self-constructing RBF neural network (SC-RBFNN) which is implemented by employing dynamically constructive hidden nodes according to the structure learning criteria including hidden node generating and pruning. The globally asymptotical stability of the entire AS-RBFNC control system is derived from Lyapunov approach.

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