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光电精跟踪系统的改进差分进化算法研究

董全睿 陈涛 高世杰 刘永凯 张建强 吴昊

董全睿, 陈涛, 高世杰, 刘永凯, 张建强, 吴昊. 光电精跟踪系统的改进差分进化算法研究[J]. 中国光学, 2020, 13(6): 1314-1323. doi: 10.37188/CO.2020-0021
引用本文: 董全睿, 陈涛, 高世杰, 刘永凯, 张建强, 吴昊. 光电精跟踪系统的改进差分进化算法研究[J]. 中国光学, 2020, 13(6): 1314-1323. doi: 10.37188/CO.2020-0021
DONG Quan-rui, CHEN Tao, GAO Shi-jie, LIU Yong-kai, ZHANG Jian-qiang, WU Hao. Identification of opto-electronic fine tracking systems based on an improved differential evolution algorithm[J]. Chinese Optics, 2020, 13(6): 1314-1323. doi: 10.37188/CO.2020-0021
Citation: DONG Quan-rui, CHEN Tao, GAO Shi-jie, LIU Yong-kai, ZHANG Jian-qiang, WU Hao. Identification of opto-electronic fine tracking systems based on an improved differential evolution algorithm[J]. Chinese Optics, 2020, 13(6): 1314-1323. doi: 10.37188/CO.2020-0021

光电精跟踪系统的改进差分进化算法研究

doi: 10.37188/CO.2020-0021
基金项目: 国家重点研发计划资助项目(No. 2016YFB0500100);长光复旦联合基金(No. Y8O732E);民用航天预研项目(No. D04010)
详细信息
    作者简介:

    董全睿(1992—),男,吉林长春人,博士研究生,2014年于吉林大学获得学士学位,主要从事光电精密跟踪测量技术方面的研究。E-mail:dongquanrui0431@126.com

    陈涛:陈 涛(1965—),男,内蒙古赤峰人,工学博士,研究员,博士生导师,2007 年于中国科学院长春光学精密机械与物理研究所获得博士学位,主要从事光电跟踪伺服控制技术与光电测控系统总体技术研究。E-mail:chent@ciomp.ac.cn

  • 中图分类号: TP13

Identification of opto-electronic fine tracking systems based on an improved differential evolution algorithm

Funds: Supported by National Key R & D Program of China (No. 2016YFB0500100); Fudan University-CIOMP Joint Fund (No. Y8O732E); Civil Aerospace Pre-research Project (No. D04010)
More Information
  • 摘要: 针对激光通信精跟踪系统,提出一种基于改进差分进化算法的辨识方法。首先,介绍了标准差分进化算法的基本原理和算法流程,基于此提出一种改进的差分进化算法,并对算法中的参数进行优化;其次,通过扫频信号激励精跟踪系统分析被控对象的动态特性,同时采集CCD相机的位置反馈信息;最后,根据实验数据采用差分进化算法对系统进行辨识,获得精跟踪系统的控制模型。实验结果表明:采用改进差分进化算法后,辨识方法的收敛速度更快,辨识结果准确,该方法在光电跟踪领域有一定工程价值。
  • 图  1  差分进化算法流程图

    Figure  1.  Flowchart of differential evolution algorithm

    图  2  4种算法在6个Benchmark函数上的适应度收敛曲线

    Figure  2.  Fitness convergence curves of four different algorithms applied to 6 Benchmark functions

    图  3  实验平台示意图

    Figure  3.  Schematic diagram of experimental platform

    图  4  快速反射镜结构简图

    Figure  4.  Simplified structure of FSM

    图  5  精跟踪系统开环输入-输出响应图

    Figure  5.  Response graph of input-output for the fine tracking system in open loop

    图  6  标准差分算法和自适应差分算法辨识结果对比

    Figure  6.  Comparison of identification results by using the traditional algorithm and adaptive difference algorithm

    图  7  差分进化算法辨识输出与系统实际输出结果比较

    Figure  7.  Identification output of the differential evolution algorithm compared with the actual output of the system

    图  8  辨识模型的频率特性比较曲线

    Figure  8.  Frequency characteristic comparison curve of the identification model

    表  1  6个Benchmark函数

    Table  1.   Six kinds of Benchmark test functions

    函数公式最优解取值范围
    Sphere$\displaystyle\sum\limits_{i = 1}^D { {x_i}^2}$0[−100, 100]
    Quadric${\displaystyle\sum\limits_{i = 1}^D {\left( {\sum\limits_{j = 1}^i { {x_i} } } \right)} ^2}$0[−100, 100]
    Rosenbrock$\displaystyle\sum\limits_{i = 1}^D {\left[ {100{ {\left( {x{}_{i + 1} - {x_i}^2} \right)}^2} + { {\left( {1 - {x_i} } \right)}^2} } \right]}$0[−30, 30]
    Rastrigin$\displaystyle\sum\limits_{i = 1}^D {\left[ { {x_i}^2 - 10\cos \left( {2{\text{π}} {x_i} } \right) + 10} \right]}$0[−5.12, 5.12]
    Griewank$\dfrac{1}{ {4\;000} }\displaystyle\sum\limits_{i = 1}^D { {x_i}^2 - \mathop \prod \limits_{i = 1}^D } \cos \left( {\frac{ { {x_i} } }{ {\sqrt i } } } \right) + 1$0[−600, 600]
    Ackley$- 20\exp \left( { - 0.2\sqrt {\dfrac{1}{n}\displaystyle\sum\limits_{i = 1}^D { {x_i}^2} } } \right) - \exp \left( {\dfrac{1}{D}\displaystyle\sum\limits_{i = 1}^D {\cos \left( {2{\text{π}} {x_i} } \right) + 20 + e} } \right)$0[−32, 32]
    下载: 导出CSV

    表  2  算法精度测试结果

    Table  2.   Accuracy of the algorithm’s test results

    函数PSOGADEADE
    MeanStdMeanStdMeanStdMeanStd
    Sphere48.925.70.250.134.82e-228.78e-233.9e-405.9e-41
    Quadric3.56e+63.05e+67.7e+43.23e+47.3e+48.62e+35.31e-33.42e-3
    Rosenbrock88.755.63.36e+31.32e+378.336.71.65e-41.23e-4
    Rastrigin9.756.968.653.944.36e+22.32e+22.32e-36.78e-4
    Griewank76.536.41.360.613.36e-31.32e-34.56e-156.23e-15
    Ackley60.240.546.633.88.65e-55.41e-51.65e-125.65e-13
    下载: 导出CSV

    表  3  两种算法的辨识结果比较

    Table  3.   Comparison of identification results by using two algorithms

    辨识方法标准差分进化算法改进差分进化算法
    a017.6216.18
    b00.0270.028
    b110.29.37
    Te0.0030.003
    RMS4.21×1041.93×104
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-11
  • 修回日期:  2020-03-25
  • 网络出版日期:  2020-10-15
  • 刊出日期:  2020-12-01

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