Neural network based self-tuning control of a switched reluctance motor drive to maximize torque per ampere

On-line self-tuning control is essential to optimize the performance of a Switched Reluctance Motor (SRM) Drive in the presence of parameter variations. This paper introduces an advanced adaptive Neural Network (NN) based control to maximize torque per ampere in the low speed region. The proposed control technique utilizes a heuristic search method to find the change in the optimal excitation instances in case of parameter variations. Based on the results of this heuristic search, the NN employs an incremental learning to adapt its network weights. Computer simulations are performed to verify the applicability of the proposed algorithm. Experimental results are provided to demonstrate the working of the self-tuning controller.