Performance optimization of joint permanent magnet synchronous motor based on improved gray wolf algorithm

Abstract Permanent magnet synchronous motors (PMSMs) are the preferred choice for robot joint drives. Non-singular fast terminal sliding mode control (NFTSMC) can significantly enhance the robustness and control performance of PMSMs. Nevertheless, it requires the manual tuning of up to ten control parameters, and traditional tuning methods struggle to identify the optimal settings. Therefore, this paper proposes an NFTSMC optimization scheme for PMSM based on an improved gray wolf optimization (IGWO) algorithm. To ensure the GWO finds the optimal solution quickly in this application scenario, two enhancement strategies have been selected. In addition, a comprehensive evaluation indicator is proposed, which combines performance indicators such as response time and overshoot to guide the algorithm in achieving the desired control performance. Finally, an algorithm deployment scheme is proposed to achieve online performance optimization. The proposed approach has been experimentally validated using a fast-prototyping framework. The experimental results demonstrate that the proposed solution can quickly identify control parameters that meet the requirements under the guidance of the proposed evaluation indicator. Comparative experiments also confirm the superiority of the IGWO algorithm in this application.