A Novel Hybrid Recurrent Wavelet Neural Network Control for a PMSM Driven Electric Scooter

Abstract

The electric scooter with nonlinear friction force of the transmission belt made the hybrid recurrent neural network (HRNN) control system with degenerated tracking responses. In order to overcome this problem, a hybrid recurrent wavelet neural network (HRWNN) control system is proposed to control for a permanent magnet synchronous motor (PMSM) driven electric scooter. The HRWNN control system consists of a supervisor control, a RWNN and a compensated control with adaptive law. The on-line parameter training methodology of the RWNN can be derived using adaptation laws and the Lyapunov stability theorem. The RWNN has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. To show the effectiveness of the proposed controller, comparative studies with HRNN control system is demonstrated by experimental results.