A Novel Wavelet-Neural-Network-Based Robust Controller for IPM Motor Drives

Abstract

This paper presents a novel wavelet-neural-network (NN) (WNN)-based speed controller for interior permanent-magnet (IPM) motor drives. The speed error and its change are used as inputs to the proposed controller. The WNN controller parameters such as weights, translation, and dilation of the wavelet functions are regulated adaptively with speed and load. The back-propagation-based training algorithm is used for updating the WNN parameters. The Lyapunov stability is used for stable learning and robustness of the WNN controller. The IPM motor is operated above the rated speed using the flux-weakening control. The maximum-torque-per-ampere control is used below the rated speed. The proposed WNN-controller-based IPM drive is implemented using the MATLAB/Simulink software and the dSPACE digital signal processor hardware. The performance of the WNN controller is compared to those of the conventional proportional–integral (PI)-, PI–derivative-, and NN-based speed controllers. The WNN controller is found more robust and quicker than conventional controllers.