Emotional Learing Based Intelligent Speed Controller Applied to Neurofuzzy Model of Switched Reluctance Motor

In this paper, an intelligent controller is applied to speed control of a switched reluctance motor. First, the produced torque of motor, as a nonlinear function, is identified using an efficient algorithm of training for Locally Linear Neurofuzzy Models (LoLiMoT). Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of a limbic system in the mammalian brain. The Brain Emotional Based Learning Intelligent Controller (BELBIC) based on PID control is adopted for the switched reluctance motor. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for BELBIC.

Keywords:

Intelligent control , emotion based learning , neurofuzzy models , switched reluctance motor