Fuzzy model reference learning control of induction motor via genetic algorithms

This paper presents an optimization technique for a model reference learning fuzzy controller using genetic algorithms. It consists of developing an algorithm that searches for the optimal parameters of an adaptive fuzzy indirect field oriented control of an induction motor. The optimized parameters are those that are not subjected to adaptation or learning processes during system operation. The proposed algorithm minimizes the effort and the time consumed during the design phase. It also guarantees a control design with the best performances that can be achieved under motor parameters variation/uncertainty and in a field weakening regime