Adaptive Self-constructing Radial-Basis-Function Neural Control for MIMO Uncertain Nonlinear Systems with Unknown Disturbances
Release Time:2019-03-11 Hits:
Indexed by: Conference Paper
Date of Publication: 2014-07-06
Included Journals: Scopus、CPCI-S、EI
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.