Implementation of permanent magnet synchronous motor servo system with novel adaptive inverse control

Abstract

A novel adaptive inverse control strategy is proposed for the permanent magnet synchronous motor (PMSM) servo system which is nonlinear, multi-variables and strong coupling. This control strategy adopts modified radial basis function (RBF) neural network and FIR filter as nonlinear filter. The proposed filter is used to identify the plant, inverse plant and designed the adaptive inverse controller. Meanwhile modified variable step size LMS algorithm is used to optimize the parameters of the nonlinear filter online. The proposed control strategy improved the convergence speed and accuracy, further improved the control performance of adaptive inverse control. The simulation and experiment results validate that the PMSM servo system has good dynamic, static performance including transient responses, parameters disturbance responses and tracking responses.