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

Area of Science:

• Electrical Engineering
• Control Systems

Background:

• Permanent magnet synchronous motors (PMSMs) are widely used in various applications.
• Robust and precise control of PMSMs is crucial for performance and efficiency.
• Traditional control methods often struggle with parameter uncertainties and external disturbances, leading to chattering and reduced transient performance.

Purpose of the Study:

• To propose a novel adaptive-gain generalized super twisting algorithm for PMSMs.
• To enhance the robustness and transient performance of PMSM control systems.
• To reduce chattering in the control output.

Main Methods:

• Development of an adaptive-gain generalized super twisting algorithm.
• Strict stability proof using the Lyapunov method.
• Design of speed-tracking and current regulation controllers using the proposed algorithm.
• Application of a filtered high-gain observer for disturbance estimation.
• Experimental validation comparing the proposed algorithm with a fixed-gain approach.

Main Results:

• The proposed adaptive-gain algorithm demonstrates improved transient performance and system robustness.
• Chattering in the control system is significantly reduced.
• The filtered high-gain observer effectively estimates lumped disturbances, enhancing overall system stability.
• Experimental results confirm the superiority of the adaptive-gain approach over fixed-gain methods.

Conclusions:

• The novel adaptive-gain generalized super twisting algorithm offers significant advantages for PMSM control.
• The control scheme effectively enhances robustness against parameter uncertainties and load disturbances.
• The method provides a promising solution for high-performance PMSM applications requiring reduced chattering.