期刊文献+

永磁同步电机PID参数优化研究 被引量:13

Research on Parameters Optimization of PMSM Speed Servo System
在线阅读 下载PDF
导出
摘要 研究永磁同步电机优化控制问题,永磁同步电机具有强耦合和强非线性特性的特点,应用环境一般较为复杂且常常存在各种干扰使电机系统稳定性差,针对传统PID控制方式很难满足电机系统要求,控制效果差,超调大。为提高控制精度,提出一种改进的PID控制方法。将PID控制器的参数作为粒子群中的粒子,系统控制精度作为粒子的寻优目标,通过粒子搜索找到最优PID控制参数,从而对电机进行精确的控制。仿真结果表明,粒子群算法的PID控制器提高了永磁同步电机系统控制精度,为永磁同步电机优化设计提供了科学依据。 Research about PID parameters optimization problem.Motor speed control system is a complex system,changes with time,and has the characteristics of nonlinear and strong coupling,therefore,the traditional PID parameters optimization method can not obtain the optimal PID parameters,and the system control effect is poor.In order to improve the control precision,the paper put forward a PID parameters optimization method based on particle swarm algorithm.PID controller parameters were taken as the particles while the system control accuracy as particle swarm optimum target.The optimal PID control parameters were found by through particle search,thus improved the performances of motor speed control system.Simulation results show that PID controller based on particle swarm algorithm improves the system control accuracy and adjusting speed,reduces the overshoots,and has a good application prospect.
作者 鲍建成
出处 《计算机仿真》 CSCD 北大核心 2012年第4期247-250,共4页 Computer Simulation
关键词 粒子群算法 永磁电机 比例积分微分控制器 参数优化 PSO PMSM PID Parameters optimization
  • 相关文献

参考文献9

二级参考文献21

  • 1吴建生,金龙,农吉夫.遗传算法BP神经网络的预报研究和应用[J].数学的实践与认识,2005,35(1):83-88. 被引量:52
  • 2陈福祥,杨芝雄.PID调节器自整定的PM法及其公式推导[J].自动化学报,1993,19(6):736-740. 被引量:44
  • 3王晓东,陈伯时,夏承光.基于单神经元自适应PID控制器直流调速系统的研究[J].电气传动,1996,26(4):29-32. 被引量:20
  • 4陈隆昌等编著.控制电机[M].西安电子科技大学出版社,2001..
  • 5K J Astrom, T Hagglund. The future of PID control[J]. Control Engineering Practice, Nov 2001,9( 11 ) : 1163 - 1175.
  • 6J Lee. On method for improving performance of PI - type fuzzy logic controllers[ J]. IEEE Trans Fuzzy Systems, 1993, 1(4) :298 - 301.
  • 7HYing. A nonlinear fuzzy controller with linear control rules is the sum of global two - dimensional multi - level relay and a local nonlinear PI controller[ J ]. Automatiea, 1993, 29 (2) :499 - 505.
  • 8Holland J H. Adaptation in nature and artificial system [M].Ann Arbor: The University of Michigan Press, 1975.
  • 9FUNG R F, LIN F J, WAI R J,et al. Fuzzy neural network control of a motor-quick-return servomechanism [J].Mech-atronics, 2000,10(1-2) : 145-167.
  • 10Eric R Motto. Application specific intelligent power modules-A novel approach to system integration in low power drives [K]. Powerex Inc. ASIPM Application Manual.

共引文献48

同被引文献105

引证文献13

二级引证文献48

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部