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LPV模型的动态压缩测量辨识算法

邱棚 李鸣谦 姚旭日 翟光杰 王雪艳

邱棚, 李鸣谦, 姚旭日, 等 . LPV模型的动态压缩测量辨识算法[J]. 北京航空航天大学学报, 2019, 45(5): 961-969. doi: 10.13700/j.bh.1001-5965.2018.0487
引用本文: 邱棚, 李鸣谦, 姚旭日, 等 . LPV模型的动态压缩测量辨识算法[J]. 北京航空航天大学学报, 2019, 45(5): 961-969. doi: 10.13700/j.bh.1001-5965.2018.0487
QIU Peng, LI Mingqian, YAO Xuri, et al. Dynamic compression measurement identification algorithm of LPV model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(5): 961-969. doi: 10.13700/j.bh.1001-5965.2018.0487(in Chinese)
Citation: QIU Peng, LI Mingqian, YAO Xuri, et al. Dynamic compression measurement identification algorithm of LPV model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(5): 961-969. doi: 10.13700/j.bh.1001-5965.2018.0487(in Chinese)

LPV模型的动态压缩测量辨识算法

doi: 10.13700/j.bh.1001-5965.2018.0487
基金项目: 

国家自然科学基金 61605218

中国科学院国防科技创新基金 CXJJ-17S023

详细信息
    作者简介:

    邱棚  男, 博士研究生。主要研究方向:压缩感知、系统辨识、模型预测控制

    李鸣谦  男, 博士研究生。主要研究方向:超声成像、声音定位; 姚旭日男, 副研究员。主要研究方向:鬼成像、超分辨成像、压缩成像

    姚旭日  男, 副研究员。主要研究方向:鬼成像、超分辨成像、压缩成像

    翟光杰  男, 研究员。主要研究方向:极弱光探测、计算成像、微重力科学

    王雪艳女, 高级实验师。主要研究方向:机器人运动控制、控制器设计

    通讯作者:

    翟光杰, E-mail:gjzhai@nssc.ac.cn

  • 中图分类号: V19;TP271+.7

Dynamic compression measurement identification algorithm of LPV model

Funds: 

National Natural Science Foundation of China 61605218

National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciences CXJJ-17S023

More Information
  • 摘要:

    在解决线性参变(LPV)模型的辨识问题上,最小二乘算法以结构简单、计算复杂度低等优点被大量使用。但最小二乘算法辨识结果受制于计算精度和模型近似精度,而这两者在同一个系统中是互斥的。因此,该算法的辨识结果与真值总是存在一定的误差。另外,在高阶LPV模型辨识或采样成本高的情况下,一般模型参数要多于辨识数据,而此时最小二乘算法很难得到稳定的辨识结果。本文提出的动态压缩测量辨识(DCMI)算法从两个方面提高在该情况下的系统辨识精度。其一,利用“匀速变化”及“非匀速变化”模型表示参变函数,以提高模型近似精度。其二,利用压缩感知理论的欠采样能力,在同等数据量的情况下提高参数的计算精度、扩大模型的计算规模。仿真结果表明,基于“匀速变化”模型DCMI算法可以准确地辨识出LPV函数,而且该算法在辨识数据不足的情况下仍然能够获得稳定的辨识结果。

     

  • 图 1  LPV模型真值和不同算法辨识结果对比

    Figure 1.  Comparison between truth value of LPV model and identification result of different algorithms

    图 2  测量数对不同算法预测精度的影响

    Figure 2.  Influence of measurement number on prediction accuracy of different algorithms

    图 3  观测噪声对不同算法预测精度的影响

    Figure 3.  Influence of measurement noise on prediction accuracy of different algorithms

    图 4  线性变化情况下测量数对不同算法预测精度的影响

    Figure 4.  Influence of measurement number on output prediction accuracy of different algorithms in case of linear variation

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出版历程
  • 收稿日期:  2018-08-29
  • 录用日期:  2018-12-07
  • 刊出日期:  2019-05-20

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