Adaptive H_∞-Based Variable Structure Control for Permanent-Magnet Synchronous Motor-Driven Uncertain Linear Stage via Self-Learning Recurrent Fuzzy-Wavelet-Neural-Network

This paper proposes a robust adaptive variable structure H∞ control (RAVSHC) using self-learning recurrent-fuzzy-wavelet-neural-network (SLRFWNN) for permanent-magnet synchronous motor (PMSM)-driven linear stage with a ball-screw. The RAVSHC scheme incorporates a variable structure controller (VSC) and a SLRFWNN uncertainty estimator with H∞-tracking design technique. However, the uncertainty terms due to friction and backlash nonlinearities of the ball-screw as well as the parameter variations and nonlinearities of the PMSM can destroy the performance of the linear stage seriously. Therefore, the SLRFWNN is utilized to approximate these uncertain terms and the H∞-tracking is used to compensate the effect of the residual approximation errors of the SLRFWNN. Furthermore, the online adaptive control laws are derived using the Lyapunov stability analysis and H∞ control theory, so that the stability of the RAVSHC can be guaranteed. The experimental results confirm that the proposed RAVSHC can achieve favorable tracking performance regardless of parameter uncertainties and compounded disturbances.