Volume 13 Issue 6
Dec.  2020
Turn off MathJax
Article Contents
Chinese Optics  > 2020  >  13(6): 1314-1323.
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
shu

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

cstr: 32171.14.CO.2020-0021
  • 1.

    Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

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
  • Corresponding author: chent@ciomp.ac.cn
  • Received Date: 11 Feb 2020
  • Rev Recd Date: 25 Mar 2020
    • Available Online: 15 Oct 2020
  • Publish Date: 01 Dec 2020
    Abstract
  • In this paper, an identification method based on an improved differential evolution algorithm is proposed for laser communication fine tracking systems. Firstly, the basic principle and calculation steps of the traditional differential evolution algorithm are introduced. Based on this, an improved algorithm is proposed, and the algorithm’s parameters are optimized . Then, the dynamic characteristics of a controlled object in the fine tracking system are simulated by a sweep signal, and the positional feed back information of the camera is collected. Finally, based on the experimental data, the differential evolution algorithm is used to identify the system, and the control model of the fine tracking system is obtained. The experimental results show that the improved differential evolution algorithm has faster convergence speed and accurate identification results. In general, this method has engineering value in the field of optoelectronic tracking.

     

    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    董全睿, 陈涛, 高世杰, 等. 星载激光通信技术研究进展[J]. 中国光学,2019,12(6):1260-1270. doi: 10.3788/co.20191206.1260

    DONG Q R, CHEN T, GAO SH J, et al. Progress of research on satellite-borne laser communication technology[J]. Chinese Optics, 2019, 12(6): 1260-1270. (in Chinese) doi: 10.3788/co.20191206.1260
    [2]
    张政江, 孙优贤. 基于阶跃响应的非自衡对象预测控制[J]. 控制与决策,2001,16(3):378-379.

    ZHANG ZH J, SUN Y X. Predictive control algorithm of integrating plant based on step-response[J]. Control and Decision, 2001, 16(3): 378-379. (in Chinese)
    [3]
    YIN H H, ZHU ZH F, DING F. Model order determination using the Hankel matrix of impulse responses[J]. Applied Mathematics Letters, 2011, 24(5): 797-802. doi: 10.1016/j.aml.2010.12.046
    [4]
    陈恒杰, 薛航, 李邵雄, 等. 一种通过约瑟夫森结非线性频率响应确定微波耗散的方法[J]. 物理学报,2019,68(11):118501.

    CHEN H J, XUE H, LI SH X, et al. A method of determining microwave dissipation of Josephson junctions with non-linear frequency response[J]. Acta Physica Sinica, 2019, 68(11): 118501. (in Chinese)
    [5]
    唐志荣, 刘明哲, 蒋悦, 等. 基于典型相关分析的点云配准算法[J]. 中国激光,2019,46(4):0404006. doi: 10.3788/CJL201946.0404006

    TANG ZH R, LIU M ZH, JIANG Y, et al. Point cloud registration algorithm based on canonical correlation analysis[J]. Chinese Journal of Lasers, 2019, 46(4): 0404006. (in Chinese) doi: 10.3788/CJL201946.0404006
    [6]
    李红云, 云利军, 高银. 基于边界限制加权最小二乘法滤波的雾天图像增强算法[J]. 中国激光,2019,46(3):0309002. doi: 10.3788/CJL201946.0309002

    LI H Y, YUN L J, GAO Y. Fog image enhancement algorithm based on boundary-limited weighted least squares filtering[J]. Chinese Journal of Lasers, 2019, 46(3): 0309002. (in Chinese) doi: 10.3788/CJL201946.0309002
    [7]
    周向阳, 朱军, 时延君. 轻小型无人机云台机电多目标优化[J]. 光学 精密工程,2018,26(11):2754-2763. doi: 10.3788/OPE.20182611.2754

    ZHOU X Y, ZHU J, SHI Y J. Multi-objective optimization on mechatronic system of a light and small pan-tilt system for unmanned aerial vehicle application[J]. Optics and Precision Engineering, 2018, 26(11): 2754-2763. (in Chinese) doi: 10.3788/OPE.20182611.2754
    [8]
    XIA X W, GUI L, YU F, et al. Triple archives particle swarm optimization[J]. IEEE Transactions on Cybernetics, 2019. doi: 10.1109/TCYB.2019.2943928
    [9]
    BENSINGH R J, MACHAVARAM R, BOOPATHY S R, et al. Injection molding process optimization of a Bi-aspheric lens using hybrid Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO)[J]. Measurement, 2019, 134: 359-374. doi: 10.1016/j.measurement.2018.10.066
    [10]
    张泉, 尹达一, 张茜丹. 压电执行器动态迟滞建模与LQG最优控制器设计[J]. 光学 精密工程,2018,26(11):2744-2753. doi: 10.3788/OPE.20182611.2744

    ZHANG Q, YIN D Y, ZHANG X D. Dynamic hysteresis modeling and LQG optimal controller design of piezoelectric actuators[J]. Optics and Precision Engineering, 2018, 26(11): 2744-2753. (in Chinese) doi: 10.3788/OPE.20182611.2744
    [11]
    MALLIPEDDI R, SUGANTHAN P N, PAN Q K, et al. Differential evolution algorithm with ensemble of parameters and mutation strategies[J]. Applied Soft Computing, 2011, 11(2): 1679-1696. doi: 10.1016/j.asoc.2010.04.024
    [12]
    MOHANTY B, PANDA S, HOTA P K, et al. Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system[J]. International Journal of Electrical Power &Energy Systems, 2014, 54: 77-85.
    [13]
    DEMERTZIS K, ILIADIS L. Adaptive elitist differential evolution extreme learning machines on big data: intelligent recognition of invasive species[C]. Proceedings of the 2nd INNS Conference on Big Data, Springer, 2016: 333-345.
    [14]
    DENG CH SH, ZHAO B Y, YANG Y L, et al.. Novel binary differential evolution without scale factor F[C]. Proceedings of the 3rd International Workshop on Advanced Computational Intelligence, IEEE, 2010: 250-253.
    [15]
    骆晨钟, 邵惠鹤. 采用混沌变异的进化算法[J]. 控制与决策,2000,15(5):557-560.

    LUO CH ZH, SHAO H H. Evolutionary algorithms with chaotic mutations[J]. Control and Decision, 2000, 15(5): 557-560. (in Chinese)
    [16]
    QU B Y, SUGANTHAN P N, LIANG J J. Differential evolution with neighborhood mutation for multimodal optimization[J]. IEEE Transactions on Evolutionary Computation, 2012, 16(5): 601-614. doi: 10.1109/TEVC.2011.2161873
    [17]
    AL-GHANIMI A, ZHENG J, MAN Z. A fast non-singular terminal sliding mode control based on perturbation estimation for piezoelectric actuators systems[J]. International Journal of Control, 2017, 90(3): 480-491. doi: 10.1080/00207179.2016.1185157
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(3)

    Get Citation
    PDF
    XML
    Article views(2622) PDF downloads(109) Cited by()
    Proportional views
    Related

    Copyright© 2012 Editorial Office of Chinese Optics    吉ICP备11002662号

    Address: No. 3888 Southeast Lake Road, Changchun City, Jilin ProvinceTel: 投稿问题：0431-84627061（李老师）；财务问题：0431-86176852（王老师）；邮寄及发行：0431-86176862（张老师）

    Email: chineseoptics@ciomp.ac.cnChina Pos: 130033

    Supported by: Beijing Renhe Information Technology Co. Ltd  support: info@rhhz.net

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return