Speed control of switched reluctance motor (SRM) using emotional learning based adaptive controller

In this paper, an intelligent controller is applied to speed control of a switched reluctance motor. Two techniques are used which have been successfully used in other intelligent modeling and control applications. Firstly, a neuro-fuzzy locally linear model tree system is used for data driven modeling of the switched reluctance motor. Secondly, a neural computing technique based on a mathematical model of amygdala and the limbic system is used for emotional control of the switched reluctance motor. The obtained results indicate the applicability of the proposed techniques in intelligent control of this highly nonlinear system.