Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive

In the paper an adaptive recurrent fuzzy neural network (ARFNN) 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 ARFNN control system is proposed to control the rotor of the SynRM servo drive for the tracking of periodic reference inputs. In the ARFNN control system, the RFNN controller is used to mimic an optimal control law, and the compensated controller with adaptive algorithm is proposed to compensate for the difference between the optimal control law and the RFNN controller. Moreover, an online 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 simulated and experimental results.