Soft computing techniques applications to a fuzzy logic control system for an induction motor drive

Presents the automatic optimal design of a fuzzy controller for an induction motor drive with vector control using two different soft-computing techniques: one based on a genetic learning strategy, the other on an adaptive network-based fuzzy inference system algorithm. These techniques perform an automatic tuning strategy for the choice of the optimal parameters values and structures for the fuzzy controller. As a result, two different controllers are obtained. They are compared in terms of design features. Computer simulations have been carried out to compare the new controllers performances to those of a PI-based controller.