An Investigation of Adaline for Torque Ripple Minimization in Non-Sinusoidal Synchronous Reluctance Motors

This paper presents a new method based on Artificial Neural Networks to obtain the optimal currents, for reducing the torque ripple in a Non-sinusoidal Synchronous Reluctance Motor. Optimal current control has to develop a constant electromagnetic torque and minimize the ohmic losses. In d-q reference frame without homopolar current, the direct and quadrature optimal currents will be determined thanks to Lagrange optimization. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents. Thanks to learning capacity of neural networks, the optimal currents will be obtained online. With this neural control, either machine's parameters estimation errors or current controller errors can be compensated. Simulation results using Matlab/Simulink are presented to confirm the validity of the proposed method.