Synchronous reluctance motor speed drive using sliding mode controller based on Gaussian radial basis function neural network

摘要

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for a synchronous reluctance motor (SynRM) system robust stabilization and disturbance rejection. This method utilizes the Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM drive system asymptotically. Finally, we employ experiments to validate the proposed method.