NSSC OpenIR  > 空间技术部
LPV模型的动态压缩测量辨识算法
Alternative TitleDynamic compression measurement identification algorithm of LPV model
邱棚; 李鸣谦; 姚旭日; 翟光杰; 王雪艳
Department空间技术部
Source Publication北京航空航天大学学报
2019
Volume45Issue:5Pages:961-969
DOI10.13700/j.bh.1001-5965.2018.0487
ISSN1001-5965
Language中文
Keyword系统辨识 压缩感知 线性参变(LPV) 线性时变(LTV) 正交匹配追踪(OMP)
Abstract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。
Other AbstractIn solving the identification problem of linear parametric variation (LPV) model, the least squares algorithm is widely used due to the advantages of simple structure and low computational complexity. However, the results of least squares algorithm are subject to computational accuracy and model approximation accuracy, which are mutually exclusive in the same system. Therefore, there is always a certain error between the identification result and the true value of the algorithm. In addition, in the case of high-order LPV model identification or high sampling cost, the general model parameters are much more than the identification data. Consequently, it is difficult for the least squares algorithm to obtain stable identification results. The dynamic compression measurement identification (DCMI) algorithm proposed in this paper improves the system identification accuracy in this case from two aspects. First, the "uniform motion" and "non-uniform motion" models are used to represent the parametric function to improve the approximate accuracy of the model. Second, the under-sampling ability of the compressed sensing theory is utilized to improve the calculation accuracy of the parameters and expand the calculation scale of the model in the case of the same amount of data. The simulation results show that the proposed DCMI algorithm based on the "uniform motion" model can accurately identify the linear parametric function. Even in the case of insufficient identification data, the algorithm can still obtain stable identification results.
Indexed ByEI ; CSCD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7073
Collection空间技术部
Recommended Citation
GB/T 7714
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.
APA 邱棚,李鸣谦,姚旭日,翟光杰,&王雪艳.(2019).LPV模型的动态压缩测量辨识算法.北京航空航天大学学报,45(5),961-969.
MLA 邱棚,et al."LPV模型的动态压缩测量辨识算法".北京航空航天大学学报 45.5(2019):961-969.
Files in This Item:
File Name/Size DocType Version Access License
20195961-969.pdf(2632KB)期刊论文出版稿开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[邱棚]'s Articles
[李鸣谦]'s Articles
[姚旭日]'s Articles
Baidu academic
Similar articles in Baidu academic
[邱棚]'s Articles
[李鸣谦]'s Articles
[姚旭日]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[邱棚]'s Articles
[李鸣谦]'s Articles
[姚旭日]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.