基于PSO算法的直接驱动阀用音圈电机优化设计

吴帅; 王大彧; 邢秋君; 焦宗夏

基于PSO算法的直接驱动阀用音圈电机优化设计

北京航空航天大学自动化科学与电气工程学院, 北京 100191

基金项目: 国家科技支撑计划资助项目(2006BAF01B10-01)

详细信息

中图分类号: TP 301.6
计量
- 文章访问数: 2946
- HTML全文浏览量: 103
- PDF下载量: 791
- 被引次数: 0
出版历程
- 收稿日期: 2010-04-02
- 网络出版日期: 2011-08-30

Voice coil motor for direct drive valve optimization design by means of particle swarm optimization

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 音圈电机(VCM,Voice Coil Motor)直接驱动伺服阀(DDV, Direct Drive Valve)采用VCM直接驱动滑阀阀芯,VCM的性能对DDV整体性能起决定性作用.由于VCM的几何设计参数对性能提升存在相互矛盾,需要在允许的参数空间内寻找一组最优的参数,往往需要设计者试凑多次.在介绍VCM原理和结构的基础上,分析了各设计参数对性能的影响.定义了针对DDV应用需求的VCM优化目标函数,应用变惯性权值和带收敛因子的改进粒子群优化(PSO, Particle Swarm Optimization)算法在设计约束的多维参数空间内进行VCM优化,得到满足设计约束的最优设计参数,表明PSO算法在类似应用中具有较强工程应用价值.
- 粒子群优化 /
- 音圈电机 /
- 设计目标函数
Abstract: Voice coil motor (VCM) direct drive valve (DDV) is a type servo valve which utilize the VCM drive spool directly and its dynamic characteristic is depends on the performance of VCM. Unfortunately, some of design parameters of VCM are contradictory when ambition the best performance, it always need to trial-and-error many times by designer. Based on the introduction of principle and structure of VCM, the effect of each design parameters for the performance was analyzed, and the parameters need to be optimized synthesized was pointed out. The optimization objective function was defined under the design constraint, and the improved particle swarm optimization (PSO) methods which use inertia weight and constriction coefficient were applied for search the optimum parameters in multidimensional parameters space. The best parameters were obtained under design constraint. It also improve that PSO is an efficient way to solution these questions.
- particle swarm optimization /
- voice coil motor /
- design objective function

HTML全文

参考文献(1)

[1] 张大卫,冯晓梅.音圈电机的技术原理[J].中北大学学报:自然科学版,2006,27(3):224-228 Zhang Dawei,Feng Xiaomei.The technical principle of voice-coil actuator [J].Journal of North University of China :Natural Science Edition,2006,27(3):224-228(in Chinese) [2] Kennedy J,Eberhart R.Particle swarm optimization // IEEE International Conf on Neural Networks.Perth:IEEE Service Center,Piscataway,NJ,1995:1942-1948 [3] 周驰,高海兵,高亮,等.粒子群优化算法[J].计算机应用研究,2003(12):7-11 Zhou Chi,Gao Haibing,Gao Liang,et al.Particle swarm optimization (PSO) algorithm[J].Research of Computer Application,2003(12):7-12(in Chinese) [4] 徐静波.基于PSO算法的PID控制参数优化[J].东华大学学报:自然科学版,2007,33(1):135-138 Xu Jinbo.PID tuning by means of particle swarm optimization [J].Journal of Donghua University :Natural Science,2007,33(1):135-138(in Chinese) [5] 黄声华,陶醒世,傅光洁.音圈电机的数学模型及仿真[J].华中理工大学学报,1996,24(S2):68-71 Huang Shenghua,Tao Xingshi,Fu Guangjie.A mathematical model for the voice coil motor and the simulation[J].1996,24(S2):68-71(in Chinese) [6] 邹继斌,王骞.音圈电机的电磁场计算与分析[J].微特电机,2008(2):4-9 Zou Jibin,Wang Qian.Calculation and analysis of the electromagnetic field of voice coil actuators [J].Small & Special Electrical Machines,2008(2):4-9 [7] 陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-60 Chen Guimin,Jia Jianyuan,Han Qi.Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm[J].Journal of Xi’an Jiao Tong University,2006,40(1):53-60(in Chinese) [8] 刘暾东,陈得宝,李素文.基于自适应认知域的粒子群性能改进方法[J].模式识别与人工智能,2009,22(5):726-730 Liu Dundong,Chen Debao,Li Suwen.An improved PSO method based on adaptive cognitive domain[J].Pattern Recognition and Artificial Intelligence,2009,22(5):726-730(in Chinese) [9] Clerc M ,Kennedy J.The particle swarm:explosion,stability,and convergence in a multi-dimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73

施引文献

资源附件(0)

访问统计