Adaptive RFNN control for synchronous reluctance motor drive

Abstract

An adaptive recurrent fuzzy neural network (RFNN) control system is proposed to control a synchronous reluctance motor (SynRM) servo drive. First, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM servo drive. Then, the adaptive RFNN control system is proposed to control the rotor of the SynRM servo drive for the tracking of periodic reference inputs. In the adaptive RFNN control system, the RFNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate the difference between the optimal control law and the RFNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by some experimental results.