基于改进RAO算法的永磁同步电机2-DOF PI参数整定

Optimum 2-DOF PI Controller Design in PMSM Using Improved RAO Algorithm

摘要: 针对2-DOF PI控制器参数不易整定、RAO-1算法容易陷入局部解的缺点，本文提出一种用于永磁同步电机参数整定的改进RAO (LILRAO)算法。在RAO算法中引入透镜成像的反向学习策略应用于种群更新，避免了算法陷入局部最优；引入Tent混沌映射进行种群初始化，增强了算法全局搜索能力。在PMSM速度环中，使用2-DOF PI控制器代替传统PI控制器，提高系统跟随性和抗扰动特性；测试函数实验表明LILRAO算法的有效性；Simulink仿真表明LILRAO算法与2-DOF PI控制器相结合可以有效提高PMSM控制系统的跟随性能和抗扰动性。

Abstract: This paper presents an improved RAO algorithm, named LILRAO algorithm, to address the chal-lenges associated with difficult parameter tuning for the 2-DOF PI controller and the tendency of the RAO-1 algorithm to converge to local solutions. The proposed algorithm focuses on parameter tun-ing for a permanent magnet synchronous motor. To prevent the algorithm from being trapped in local optima, a reverse learning strategy based on lens imaging is introduced and integrated into the population update process of the RAO algorithm. Moreover, the Tent chaotic map is employed for population initialization, thereby enhancing the algorithm’s global search capability. In the PMSM speed loop, the traditional PI controller is replaced by the 2-DOF PI controller to enhance system tracking and disturbance rejection. Experimental results on test functions verify the effec-tiveness of the LILRAO algorithm, while Simulink simulations demonstrate that combining the LILRAO algorithm with the 2-DOF PI controller significantly improves the tracking performance and disturbance rejection of the PMSM control system.