Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

Abstract

This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics and the WNN owning the capability of artificial neural networks for online learning and the capability of wavelet decomposition for identification. An observed error compensator is developed to compensate the estimated error of the WNN and the adaptive law is derived according to Lyapunov theorem. The effectiveness of the proposed controller is investigated in simulation under different operating conditions. The simulation results demonstrate the proposed WNNBSSM controller can provide precise tracking performance and robust characteristics despite unknown parameter uncertainties and load disturbance. Moreover, an implemental wavemaker system is established to verify the effectiveness of the proposed control algorithm.