Metaheuristic-Optimized Third-Order Sliding Mode Control for High-Performance Speed Regulation of Permanent Magnet Synchronous Motors

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.