Fuzzy Neural Network-based Model Reference Adaptive Inverse Control for Induction Machines

Abstract

In this paper, because the induction machines are described as the plants of highly nonlinear and parameters timevarying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field oriented motion model of induction machines. The fuzzy neural network control (FNNC) is incorporated into the model reference adaptive control (MRAC), a fuzzy basis function network controller (FBNC) and a fuzzy neural network identifier (FNNI) for asynchronous motors adjustable speed system are designed. The proposed controller for asynchronous machines resolves the shortage of MRAC, and employs the advantages of FNNC and MRAC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.