基于粒子群优化算法的模糊PI控制性能研究

Research on the Performance of Fuzzy PI Control Based on Particle Swarm Optimization Algorithm

摘要: 对于传统PI控制系统，其比例常数和积分常数的整定往往依赖于人工经验，在对永磁同步电机(Permanent Magnet Synchronous Motor, PMSM)进行矢量控制时，出现了稳定性不高、控制性能欠佳等情况，于是，提出了模糊PI控制系统。而模糊控制系统中的比例因子和量化因子是经过实验耗费大量时间反复调整取值，随机性大且无法确定最佳的控制器参数，因此，采用粒子群算法来优化模糊控制系统中的比例因子和量化因子，在MATLAB/Simulink中搭建了PMSM整体的矢量控制系统，在不同工况下分析了传统PI控制系统、模糊PI控制系统、基于粒子群优化算法的模糊PI控制系统的调速性能。仿真结果表明，基于粒子群优化算法的模糊PI控制系统，能够有效地缩短调节时间、减小转速的震荡，大幅提升了稳定性。验证了此控制系统能够满足永磁同步电机精度高、输出稳定及快速响应的工作要求。

Abstract: For the traditional PI control system, the adjustment of the proportionality and integration constants often relies on manual experience, and in the vector control of the Permanent Magnet Synchronous Motor (PMSM), the stability is insufficient and the control performance is poor, thus the fuzzy PI control system is proposed. The proportional and quantization factors in the fuzzy control system are adjusted repeatedly after a large amount of time in experiments, which is random and the best controller parameters cannot be determined, therefore, the particle swarm algorithm is used to optimize the proportional and quantization factors in the fuzzy control system, and the overall vector control system of PMSM is built in MATLAB/Simulink. The control performance of the traditional PI control system, fuzzy PI control system and fuzzy PI control system based on particle swarm optimization algorithm was analyzed under various working environments. The simulation results show that the fuzzy PI control system based on the particle swarm optimization algorithm can effectively diminish the regulation time, reduce the speed oscillation and significantly improve the stability. It is verified that this control system can meet the requirements of high precision, stable output and fast response of permanent magnet synchronous motors.