基于改进粒子群算法的预测电流控制  

Predictive current control based on improved particle swarm optimization

作　　者：张旗 贾洪平[1] ZHANG Qi;JIA Hong-ping(School of electrical and information engineering,Jiangsu University,Zhenjiang 212013,Jiangsu Province,China)

机构地区：[1]江苏大学电气信息工程学院

出　　处：《信息技术》2019年第8期66-70,共5页Information Technology

摘　　要：永磁同步电机预测电流控制存在依赖电机准确模型的缺点。在实际运行中,电机参数如电感、磁链等会发生变化,导致模型不准确,产生电流静态误差,进而影响控制效果。文中运用一种改进粒子群算法实现电机参数的在线辨识,以获得准确的模型,在此基础上实现预测电流控制,从而消除电流静态误差,同时对基本粒子群算法提出了两点改进:加入可变探测矢量能够使粒子向更广阔的解空间进行探索、使用反向学习策略能够帮助粒子跳出局部最优。To predictive current control of permanent magnet synchronous motor is depend on accurate motor model.In actual operation,if motor parameters,such as inductance and flux linkage,changes,resulting in inaccurate model and static current error,will affect the control effect.In this paper,an improved particle swarm optimization(PSO)algorithm is used to realize on-line identification of motor parameters so as to obtain an accurate model.Two improvements to the basic particle swarm optimization algorithm are proposed:adding a variable probe vector allows the particle to explore a wider solution space,using opposite learning strategy can help particles jump out of local optimization.

关 键 词：永磁同步电机 粒子群算法 预测电流控制 可变探测矢量 反向学习策略 

分 类 号：TM351[电气工程—电机]