Control of Permanent Magnet Synchronous Motor Based on Adaptive Super-Twisting Sliding Mode Observer and Improved PSO
Abstract
To mitigate the chattering and limited adaptability of conventional sliding mode observers (SMOs) in position sensorless control for permanent magnet synchronous motors (PMSMs) across a wide range of operating conditions, a control strategy based on an adaptive-gain super-twisting sliding mode observer (AST-SMO) combined with improved particle swarm optimization (PSO) for speed loop PI parameter tuning is proposed. The observer incorporates a time-varying gain function, which is driven by the magnitude of the current observation error and governed by an integral-type adaptive law. This replaces the fixed-gain structure that necessitates a predetermined disturbance upper bound, thereby effectively suppressing chattering and ensuring high estimation accuracy across a wide speed range and under abrupt load changes. For speed loop control, an improved PSO algorithm with cooperative adjustment of learning factors and inertia weight is introduced for offline optimization of PI parameters, further enhancing dynamic response and anti-disturbance capability. Simulation and experimental results demonstrate that, compared with the traditional ST-SMO and LST-SMO, the proposed AST-SMO yields lower rotor position estimation errors and reduced speed fluctuations under both steady-state and transient conditions. Meanwhile, the PSO-optimized PI controller significantly shortens settling time and reduces overshoot. The proposed strategy features a simple structure and is suitable for engineering implementation.