X.J. Liu and F. Lara-Rosano
Model-reference adaptive control, neurofuzzy networks, nonlinear controller
Model-reference adaptive control with neurofuzzy methodology is derived. Associate memory network (AMN) is investigated in detail to be the possible implementation as the direct self-tuning nonlinear controller for a class of nonlinear system. The essence of the neurofuzzy controller and the local stability of the system are discussed. The performance is illustrated by examples involving both linear and nonlinear systems
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