Robust adaptive H^∞ position control via a wavelet-neural-network for a DSP-based permanent-magnet synchronous motor servo drive system

This paper proposes an adaptive wavelet-neural-network (WNN)-based H^∞ position tracking controller as a new robust motion control system for permanent-magnet synchronous motor (PMSM) servo drives. The combinations of both WNN and H^∞ controllers would insure the robustness and overcome the uncertainties of the servo drive. The new controller combines the merits of the H^∞ control with robust performance and the WNN control (WNNC), which combines the capability of neural networks for on-line learning ability and the capability of wavelet decomposition for identification ability. The on-line trained WNNC is utilised to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of the H^∞ controller. The WNNC generates an adaptive control signal to attain robust performance regardless of parameter uncertainties and load disturbances. A systematic methodology for the design of both controllers is provided. A computer simulation is developed to demonstrate the effectiveness of the proposed WNN-based H^∞ controller. An experimental system is established to validate the effectiveness of the servo drive system. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulated and experimental results confirm that the new motion controller grants robust performance and a precise dynamic response regardless of load disturbances and PMSM parameter uncertainties.