Li Taochang, Li Ang, Hou Limin
Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China.
PLoS One. 2025 Jan 30;20(1):e0318094. doi: 10.1371/journal.pone.0318094. eCollection 2025.
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.
为了解决传统永磁同步电机（PMSM）矢量控制系统对电机参数变化和负载扰动的敏感性问题，在标准模糊PI控制的基础上，提出了一种将改进的蚱蜢优化算法（GOA）与变论域模糊比例积分（PI）控制器相结合的新型控制方法。首先，通过结合柯西变异策略和均匀分布策略，增强了算法的种群多样性和全局探索能力。随后，柯西变异与基于反向学习的融合以及对最优位置的修正，进一步提高了算法逃离局部最优的能力。然后，采用改进的GOA优化变论域模糊PI控制器的伸缩因子，实现了永磁同步电机控制性能的提升。此外，为了解决传统PI控制器在电流环中常见的高转矩和电流纹波问题，采用模型预测控制（MPC）进一步提高控制性能。最后，实验结果验证了所提控制方案的有效性，展示了精确的电机速度控制、快速稳定的电流跟踪以及提高的系统鲁棒性。
