Fuzzy-neuro based optimal control of variable reluctance motor

This paper presents the integration and synthesis of neural network and fuzzy logic to design an optimal controller with good robustness properties for the variable reluctance motor (VRM) drive system. The control scheme has two levels: first, applying the neural network for the selection of the optimal excitation parameters of the current profiles, which drive the system at required operating condition of torque and speed, then, designing a nonlinear feedback controller which regulates the drive operation around this optimal excitation parameters using fuzzy logic algorithm. The implementation of these novel techniques demonstrate the capability of the fuzzy-neuro controller to come up with the highly nonlinearities presented in the VRM drive system and the ability to perform the processing algorithm of optimal control in real-time. The simulation results are presented to attest the effectiveness of the proposed controller.