Operation parameters of PMSM.

To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm’s ability to escape local optima. The improved GOA is then employed to optimize the contraction-expansion factor of the variable universe fuzzy PI controller, achieving enhanced control performance for PMSMs. Additionally, to address the high torque and current ripple issues commonly associated with traditional PI controllers in the current loop, Model Predictive Control (MPC) is adopted to further improve control performance. Finally, experimental results validate the effectiveness of the proposed control scheme, demonstrating precise motor speed control, rapid and stable current tracking, as well as improved system robustness.针对传统永磁同步电机（permanent magnet synchronous motors, PMSMs）矢量控制系统易受电机参数波动与负载扰动影响的痛点，本文在标准模糊PI控制的基础上，提出一种将改进蝗虫优化算法（Grasshopper Optimization Algorithm, GOA）与变论域模糊比例积分（PI）控制器相结合的新型控制方案。首先，通过融合柯西变异策略与均匀分布策略，提升算法的种群多样性与全局探索能力；随后，结合柯西变异与反向学习，并对最优位置进行修正，进一步增强算法跳出局部最优解的能力。将改进后的GOA用于优化变论域模糊PI控制器的伸缩因子，以提升永磁同步电机的控制性能。此外，针对传统PI控制器在电流环中普遍存在的转矩与电流纹波过高问题，本文采用模型预测控制（Model Predictive Control, MPC）进一步优化控制性能。最终通过实验结果验证了所提控制方案的有效性，其可实现精准的电机转速控制、快速稳定的电流跟踪，并提升了系统的鲁棒性。