Adaptive generalized super twisting sliding mode control for PMSMs with filtered high-gain observer

This paper proposes a novel adaptive-gain generalized super twisting algorithm for permanent magnet synchronous motors. The stability of this algorithm is strict proof using the Lyapunov method. Both controllers of the speed-tracking loop and the current regulation loop are designed according to the proposed adaptive-gain generalized super twisting algorithm. Dynamically adjusted gains in the controllers can improve the transient performance and system’s robustness while reducing chattering. A filtered high-gain observer is applied in the speed-tracking loop to estimate the lumped disturbances, including parameter uncertainties and external load torque disturbances. The estimates feeding forward to the controller further improve the robustness of the system. Meanwhile, the linear filtering subsystem reduces the sensitivity of the observer to the measurement noise. Finally, experiments are constructed using the adaptive gain generalized super twisting sliding mode algorithm and the fixed gain one, showing the effectiveness and advantages of the proposed control scheme. •An adaptive-gain generalized super twisting algorithm (GSTA) is proposed.•The stability of the proposed adaptive-gain GSTA is rigorously proven.•The proposed adaptive-gain GSTA is applied in the PMSM systems.•A filtered high-gain observer is constructed, which enhances the robustness of the system.•Experiments results show the effectiveness and advantages of the proposed control scheme.