[1]郑力新,周凯汀.基于遗传算法的双闭环系统模糊优化设计[J].华侨大学学报(自然科学版),2000,21(1):16-20.[doi:10.3969/j.issn.1000-5013.2000.01.004]
　Zheng Lixin,Zhou Kaiting.Fuzzy Optimization Design of Double Loop System Based on Genetic Algorithm[J].Journal of Huaqiao University(Natural Science),2000,21(1):16-20.[doi:10.3969/j.issn.1000-5013.2000.01.004]
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基于遗传算法的双闭环系统模糊优化设计()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第21卷
期数:
2000年第1期
页码:
16-20
栏目:
出版日期:
2000-01-20

文章信息/Info

Title:
Fuzzy Optimization Design of Double Loop System Based on Genetic Algorithm
文章编号:
1000-5013(2000)01-0016-05
作者:
郑力新; 周凯汀
华侨大学电气工程与自动化系, 泉州362011; 华侨大学电子工程系, 泉州362011
Author(s):
Zheng Lixin1;  Zhou Kaiting2
1.Dept. of Elec. Eng. & Auto., 2 Dept. of Electron. Eng., Huaqiao Univ., 362011, Quanzhou
关键词:
遗传算法;  双闭环系统;  模糊优化设计
Keywords:
genetic algorithm;  double closed loop system;  fuzzy optimization design
分类号:
TP273
DOI:
10.3969/j.issn.1000-5013.2000.01.004
摘要:
提出一种基于遗传算法的直流双闭环调速系统参数优化设计方法 .根据工程技术的要求,选用速度超调量和过渡时间作为参数优化性能指标 .将该指标用模糊隶属度函数归一化,再加权平均形成系统优化模型的目标函数 .采用计算机数值计算方法,通过仿真获得系统对应参数的动态响应曲线及其性能指标 .最后以工程设计的参数为搜索范围,以速度调节器和电流调节器的参数为染色体中的基因,通过遗传算法在搜索范围中优化这些基因,获得优化解 .实验结果表明,所设计的参数能使系统性能指标有显著提高 .
Abstract:
A method of fuzzy optimization design based on genetic algorithm is presented as a new method of parameter optimization design for DC double closed loop speed adjusting system.The method covers three steps.Firstly,speed overshoot rate and settling time are chosen as performance indice according to the demand of engineering.These indice are normalized by using fuzzy membership function and then weighted to form objective function of optimization model of the system.Secondly,the dynamic response curve of the system with corresponding parameters and peoformance indice are obtained by computerized numerical calculation and simulation.Finally,parameters of engineering design are expanded as searching space; and parameters of speed regulator and current regulator are taken as genes in chromosome.These genes in searching space are optimized to get best solution by way of genetic algorithm.As shown by experimental results,the parameters designed by this method are capable of significantly improving performance indice of the system,which proves that it is a practical and effective method.

参考文献/References:

[1] Zuo W. A genetic approach to adaptive control system design [J]. Proceedings of the Institution of Mechanical Engineers, 1997(1):15-23.
[2] Dakev N V, Whidborne J F. Evolutionary H∞ design of an electromagnetic suspension control system for a maglev vehicle [J]. Proceedings of the Institution of Mechanical Engineers, 1997(1):345-355.
[3] Trebi O, White B A. Multiobjective fuzzy genetic algorithm optimization approach to nonlinear control system design [J]. IEE Proceedings-Control Theory and Applications, 1997(2):137-142.doi:10.1049/ip-cta:19971031.
[4] Hyun J H, Lee C O. Optimization of feedback gains for a hydraulic servo system by genetic algorithms [J]. Proceedings of the Institution of Mechanical Engineers, 1998(1):395-401.
[5] Warwick K, Kang Y H. Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares [J]. Proceedings of the Institution of Mechanical Engineers, 1998(1):473-448.
[6] 郭伯农, 陆德浩, 葛渝生. 自动控制系统 [M]. 上海:上海科学技术文献出版社, 1986.1-2.
[7] 孔凡才. 自动控制原理与系统 [M]. 北京:机械工业出版社, 1995.136-137.
[8] 郑力新. 双闭环直流调速系统参数的进化计算 [J]. 华侨大学学报(自然科学版), 1998(1):245-249.
[9] Gen Mitsuo, Cheng Runwei. Genetic algorithms·engineering design [M]. New York:John Wiley·Sons Inc, 1997.60-63.

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备注/Memo

备注/Memo:
华侨大学科研基金资助项目
更新日期/Last Update: 2014-03-23