Adaptive sliding mode speed control strategy for permanent magnet direct-drive wind power generation systems based on iterative learning

To mitigate the impact of external disturbances arising from wind speed variability and the torque ripple associated with the dynamic characteristics of wind turbines during the operation of permanent magnet direct-drive (PMDD) wind energy conversion systems, this study introduces a speed control strategy that combines adaptive sliding mode control (SMC) with iterative learning. The iterative learning control (ILC) technique is applied to attenuate periodically recurring torque ripples, while a self-tuning control mechanism is incorporated to estimate and compensate for stochastic, aperiodic disturbances within the system. The integration of SMC further enhances the system’s robustness against uncertainties. A rigorous stability proof of the designed system is presented, and the proposed controller is validated through simulations in MATLAB/Simulink. Numerical results demonstrate that the proposed control strategy effectively suppresses torque ripple and improves the precision of speed regulation. Furthermore, while maintaining strong disturbance rejection and rapid dynamic response, the system achieves maximum power point tracking (MPPT) via an optimized tip-speed ratio (OTSR) strategy, thereby improving the efficiency of wind energy capture.