广西师范大学学报（自然科学版） ›› 2016, Vol. 34 ›› Issue (2): 21-27.doi: 10.16088/j.issn.1001-6600.2016.02.004

• • 上一篇    下一篇

基于改进粒子群算法的无刷电机模糊控制研究

王国宇, 黄植功, 戴明   

  1. 广西师范大学电子工程学院,广西桂林541004
  • 收稿日期:2015-07-20 出版日期:2016-06-25 发布日期:2018-09-14
  • 通讯作者: 黄植功(1970—),男,广西田东人,广西师范大学副教授。E-mail:hbypolly@mailbox.gxnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(51367005)

Fuzzy Control Research for Brushless DC Motor Basedon Improved Particle Swarm Algorithm

WANG Guoyu, HUANG Zhigong, DAI Ming   

  1. College of Electronic Engineering,Guangxi Normal University,Guilin Guangxi 541004,China
  • Received:2015-07-20 Online:2016-06-25 Published:2018-09-14

PDF (PC)

401

摘要： 为了解决在无刷直流电机控制系统中,PID调节器出现系统超调和稳定性差等问题,本文采用一种基于改进粒子群算法优化模糊控制器的速度控制算法,该算法融合粒子群算法和量子算法的优点。实验仿真结果表明:优化后的模糊控制器动态性能和静态性能都优于传统PID控制,具有很好的鲁棒性和控制精度。

关键词: 无刷直流电机, 模糊控制器, 粒子群, 量子算法, 改进粒子群

Abstract: To solve the problems of poor stability of the system PID for the regulator and overshoot in the brushless dc motor control system, this paper puts forward a kind of fuzzy controller based on improved particle swarm algorithm to optimize the speed of the controllable algorithm, which includes the advantages of particle swarm optimization (PSO) algorithm and the quantum algorithm. The experimental simulation results show that the dynamic and static performance of the optimized fuzzy controller is superior to that of traditional PID control. The new controller has very good robustness and control precision.

Key words: BLDCM, fuzzy control, PSO, quantum algorithm, improved particle swarm algorithm

中图分类号: 

  • TM341
[1] BAO Ping,XU Zhigao.The fuzzy controller with on-line self adjustable proportional factor[J].Process Automation Instrumentation,2002,8(21):20-22.
[2] NASRI M, NEZAMABADI-POUR H, MAGHFOORI M. A PSO-based optimum design of PID controller for a linear brushless DC motor[C]. Proceedings of World Academy of Science,Engineering and Technology,2007,20:211-215.
[3] 郭桃妹,应富强.基于模糊控制的挤压铸造模温控制系统研究[J].机电工程,2010,27(11):36-39.
[4] 谢俊.基于DSP的无刷直流电机控制系统的设计与仿真研究[D].武汉:武汉理工大学,2010.
[5] KENNEDY J, EBERHART R C. Particle swarms optimization[C]//Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE Press,1995:1942-1948.
[6] LIN S W,YING K C,CHEN S C,et al. Particle swarm optimization for parameter determination and feature selection of support vector machines[J]. Expert Systems with Applications,2008, 35(4):1817-1824.
[7] PANG W, WANG K P, ZHOU C G, et al. Fuzzy discrete particle swarm optimization for solving traveling saleman problem[C]//The Fourth International Conference on Computer and Information Technology. [S.l.]: IEEE, 2004:796-800.
[8] LIU Y,QIN Z,SHI Z W,et al. Center particle swarm optimization[J]. Neuro Computing,2007, 70:672-679.
[9] JARBOUI B, CHEIKH M, SIARRY P,et al. Combinatorial particle swarm optimization for partitional clustering problem[J]. Applied Mathematics and Computation,2007, 192(2):337-345.
[10] SUN J, WU X J, PALADE V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization[J]. Journal of Information Science, 2012, 193: 81-103.
[11] COELHO L S. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications, 2010, 37(2): 1676-1683.
[12] 王剑,黄植功.基于优化EKF的永磁同步电机转速估计[J].广西师范大学学报(自然科学报),2014,32(4):11-17.
[13] SUN J, FANG W, PALADE V, et al. Quantum-behaved particle swarm optimization with gaussian distributed local attractor point[J]. Applied Mathematics and Computation, 2011, 218(7): 3763-3775.
[14] LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. Evolutionary Computation, 2006, 10(3): 281-295.
[15] MARCIO S, EVARISTO C. Nonlinear parameter estimation through particle swarm optimization[J]. Chemical Engineering Science, 2008, 63(6): 1542-1552.
[1] 李飞羽, 翁小雄, 姚树申. 基于乘客群体出行时间间隔的标度律研究[J]. 广西师范大学学报（自然科学版）, 2020, 38(1): 1-9.
[2] 王剑, 黄植功, 许金海. 基于优化EKF的永磁同步电机转速估计[J]. 广西师范大学学报（自然科学版）, 2014, 32(4): 11-17.
[3] 徐胜舟, 许向阳, 胡怀飞, 李波. 基于改进动态规划的MR图像左心室分割[J]. 广西师范大学学报（自然科学版）, 2014, 32(2): 35-41.
[4] 严晓明, 郑之. 基于混合仿生算法的SVM参数优化[J]. 广西师范大学学报（自然科学版）, 2011, 29(2): 114-118.
[5] 刘炜, 王浩, 方宝富. 基于二进制粒子群的救援仿真机器人决策[J]. 广西师范大学学报（自然科学版）, 2011, 29(2): 130-133.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发