基于粒子群优化的电磁离合器模糊控制研究

中国机械工程 ›› 2010, Vol. 21 ›› Issue (9): 1071-1077.

基于粒子群优化的电磁离合器模糊控制研究

吴晓刚;王旭东;余腾伟;张宇

哈尔滨理工大学汽车电子驱动控制与系统集成教育部工程研究中心, 哈尔滨,150080

出版日期:2010-05-10 发布日期:2010-05-19
基金资助:
黑龙江省科技攻关项目(GB08A306)；黑龙江省教育厅科学技术研究项目(11531z03)
Heilongjiang Provincial Key Technology R&D Program（No. GB08A306）;
Heilongjiang Provincial Science and Technology Research Program of Ministry of Education of China（No. 11531z03）

Research on Fuzzy Control of Electromagnetic Clutch Based on Particle Swarm Optimization

Wu Xiaogang;Wang Xudong;Yu Tengwei;Zhang Yu

Harbin University of Science & Technology, Research Center of the Ministry of Education of Automotive Electronics Drive & Control and System Integration, Harbin, 150080

Online:2010-05-10 Published:2010-05-19
Supported by:

Heilongjiang Provincial Key Technology R&D Program（No. GB08A306）;
Heilongjiang Provincial Science and Technology Research Program of Ministry of Education of China（No. 11531z03）

摘要/Abstract

摘要：

针对车辆起步过程中应用传统模糊控制存在的精度不高、自适应能力有限等问题,提出了应用粒子群算法优化模糊控制器量化因子的方法。优化的量化因子随环境变化以及负载变化实时跟踪模糊控制器的参数变化,使得模糊控制器的鲁棒性和控制精度都得到了提高。仿真与实验结果表明,在车辆起步过程中,与传统的模糊控制算法相比基于粒子群优化的模糊控制算法能够在减小冲击度的同时有效减小滑摩功,具有较为理想的起步效果。

关键词:

自动变速, 电磁离合器, 粒子群, 模糊控制

Abstract:

Aiming at the problems such as lower precision and limited adaptive capability when the traditional fuzzy control is applied on a car during starts, a quantizing factor of fuzzy controller based on PSO was proposed, the optimal quantizing factor was changed to follow parameter changes of fuzzy controller as environmental changes and load changes, so the robustness and control accuracy were improved. The simulation and experimental result show the fuzzy control algorithm based on PSO compared with the traditional fuzzy control algorithm can effectively decrease the work of slipping, at the same time decrease the shock degree, and achieve at the ideal starting effect.

Key words: automatic gear shifting, electromagnetic clutch, particle swarm optimization(PSO);fuzzy control

中图分类号:

U463.211

吴晓刚, 王旭东, 余腾伟, 张宇.

基于粒子群优化的电磁离合器模糊控制研究

[J]. 中国机械工程, 2010, 21(9): 1071-1077.

TUN Xiao-Gang, WANG Xu-Dong, TU Teng-Wei, ZHANG Yu.

Research on Fuzzy Control of Electromagnetic Clutch Based on Particle Swarm Optimization

[J]. China Mechanical Engineering, 2010, 21(9): 1071-1077.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/

http://www.cmemo.org.cn/CN/Y2010/V21/I9/1071

参考文献

[1]魏英俊[1,2].车用磁粉离合器起步控制试验研究[J].中国机械工程,2007,18(11):1375-1377.
[2]王旭东[1],谢先平[1],吴晓刚[1],余腾伟[1].自动离合器起步模糊控制[J].农业机械学报,2008,39(12):18-22.
[3]Liu Haiou, Chen Huiyan, Ding Huarong. Adaptive Clutch Engaging Process Control for Automatic Mechanical Transmission[J]. Journal of Beijing Institute of Technology, 2005, 14(2)..170-174.
[4]Cristian T I. The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection[J]. Information Processing Letters (S0020- 0190), 2003, 85(6) :317-325.
[5]祁春清,宋正强[2].基于粒子群优化模糊控制器永磁同步电机控制[J].中国电机工程学报,2006,26(17):158-162.
[6]赵波[1],曹一家[1].电力系统无功优化的多智能体粒子群优化算法[J].中国电机工程学报,2005,25(5):1-7.
[7]白敏丹[1,2],韩红桂[1],乔俊飞[1].基于遗传算法的污水处理模糊控制方法[J].控制工程,2009,16(1):46-48.
[8]金义雄 [1],程浩忠 [1],严健勇 [2],张丽 [2].改进粒子群算法及其在输电网规划中的应用[J].中国电机工程学报,2005,25(4):46-50.
[9]Ghoshal S P. Optimizations of PID Gains by Particle Swarm Optimizations in Fuzzy Based Automatic Generation Control [J]. Electric Power Research, 2004,72 (3) : 203-212.
[10]Serrarens A, Dassen M, Steinbuch M. Simulation and Control of an Automotive Dry Clutch [C]// Proceedings of the 2004 American Control Conference. Boston,2004: 4078-4083.
[11]赵永胜[1],任卫群[1],张云清[1],陈立平[1].汽车AMT自动离合器的局部线性化模糊滑模控制[J].农业机械学报,2007,38(1):17-21.
[12]谢先平[1],王旭东[1],吴晓刚[1],余腾伟[1].车辆起步过程发动机恒转速自适应模糊控制研究[J].系统仿真学报,2008,20(16):4382-4386.
[13]赵和平,刘奋,张建武.变结构控制方法在电控离合器中的应用研究[J].中国机械工程,2002,13(20):1797-1800.
[14]孙冬野,秦大同.汽车离合器局部恒转速起步自动控制研究[J].机械工程学报,2003,39(11):108-112.
[15]周美兰[1],王旭东[1],周永勤[1].无级变速汽车动力传动系统控制策略研究[J].哈尔滨工业大学学报,2009,41(3):217-220.
[16]Dimopoulos G G. Mixedvariable Engineering Optimization Based on Evolutionary and Social Metaphors[J]. Computer Methods in Applied Mechanics and Engineering, 2007, 196 (4/6): 803- 817.
[17]Schutte J F, Reinboh J A, Fregly B J, et al. Parallel Global Optimization with the Particle Swarm Optimization Algorithm[J]. International Journal of Numerical Methods in Engineering (S0029 5981), 2004, 61(13): 2296-2315.