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基于鲁棒优化的系统辨识算法研究

钱富才 黄姣茹 秦新强

钱富才, 黄姣茹, 秦新强. 基于鲁棒优化的系统辨识算法研究. 自动化学报, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
引用本文: 钱富才, 黄姣茹, 秦新强. 基于鲁棒优化的系统辨识算法研究. 自动化学报, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
QIAN Fu-Cai, HUANG Jiao-Ru, QIN Xin-Qiang. Research on Algorithm for SystemIdentification Based on RobustOptimization. ACTA AUTOMATICA SINICA, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988
Citation: QIAN Fu-Cai, HUANG Jiao-Ru, QIN Xin-Qiang. Research on Algorithm for SystemIdentification Based on RobustOptimization. ACTA AUTOMATICA SINICA, 2014, 40(5): 988-993. doi: 10.3724/SP.J.1004.2014.00988

基于鲁棒优化的系统辨识算法研究

doi: 10.3724/SP.J.1004.2014.00988
基金项目: 

国家自然科学基金(61273127),高等学校博士学科点专项科研基金(20116118110008)资助

详细信息
    作者简介:

    黄姣茹 西安理工大学自动化与信息工程学院博士研究生. 主要研究方向为鲁棒优化,系统辨识和最优控制. E-mail:huangjiaoru@126.com

Research on Algorithm for SystemIdentification Based on RobustOptimization

Funds: 

Supported by National Natural Science Foundation of China (61273127), and Specialized Research Fund for the Doctoral Program of Higher Education (20116118110008)

  • 摘要: 输入-输出数据是解决系统辨识问题的关键要素,传统的辨识理论除了假定影响输入-输出数据干扰的密度函数已知外,还要假定输入-输出数据能够精确获得,完全忽略了所用数据的质量.本文突破了传统理论的两个假设,首先用工程上易于获得的干扰的有界集合代替干扰的密度函数,并在特定数据不确定性结构下,考虑了数据质量问题,然后,以半定规划为基础,导出了鲁棒对等式,从而将系统辨识转化为对数据质量具有鲁棒性的优化问题,通过求解该优化问题,得到了一种新的鲁棒优化辨识方法,仿真结果表明了新方法的可行性和有效性.
  • [1] Fang Chong-Zhi, Xiao De-Yun. Process Identification. Beijing: Tsinghua University Press, 1998(方崇智, 萧德云. 过程辨识. 北京: 清华大学出版社, 1988)
    [2] Ljung L. System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987
    [3] Chen H F. Recursive identification for Wiener model with discontinuous piece-wise linear function. IEEE Transactions on Automatic Control, 2006, 51(3): 390-400
    [4] Burnham K P, Anderson D R. Multimodel inference: understandingAIC and BIC in model selection. Sociological Methods and Research, 2004, 33(2): 261-304
    [5] Soderstrom T. Accuracy analysis of the Frisch scheme for identifying errors-in-variables systems. IEEE Transactions on Automatic Control, 2007, 52(6): 985-997
    [6] Kar S, Sinopli B, Moura J M F. Kalman filtering with intermittent observations: weak convergence to a stationary distribution. IEEE Transactions on Automatic Control, 2012,57(2): 405-420
    [7] Ge Quan-Bo, Li Wen-Bin, Sun Ruo-Yu, Xu Zi. Centralized fusion algorithms based on EKF for multisenor non-linear systems.Acta Automatica Sinica, 2013, 39(6): 816-825(葛全波, 李文斌, 孙若愚, 徐姿. 基于EKF的集中式融合估计研究.自动化学报, 2013, 39(6): 816-825)
    [8] Li D, Qian F C, Fu P L. Variance minimization approach for a class of dual control problems. IEEE Transactions on Automatic Control, 2002, 47(12): 2010-2020
    [9] Li D, Qian F C, Fu P L. Optimal nominal dual control for discrete-time LQG problem with unknown parameters. Automatica, 2008, 44(1): 119-127
    [10] Li D, Qian F C, Gao J J. Performance-first control for discrete-time LQG problems. IEEE Transactions on Automatic Control, 2009, 54(9): 2225-2230
    [11] Qian F C, Gao J J, Li D. Complete statistical characterization of discrete-time LQG and cumulant control. IEEE Transactions on Automatic Control, 2012, 57(8):2110-2115
    [12] Ben-Tal A, Nemirovski A. Robust optimization: methodology and applications. Mathematical Programming, 2002, 92(3): 453-480
    [13] Lin Xiao-Zhong, Xie Lei, Su Hong-Ye. Economic performance for predict control system under model uncertainty. Acta Automatica Sinica,2012,38(8): 1-5(林晓钟, 谢磊, 苏宏业. 模型不确定条件下预测控制经济性能评估的研究.自动化学报, 2012, 38(8):-5)
    [14] Bertsimas D, Litvinov E, Sun X A, Zhao J Y, Zheng T X. Adaptive robust optimizaition for the security constrained unit commitment problem. IEEE Transactions on Power Systems, 2013, 28(1): 52-63
    [15] Soyster A L. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operational Research, 1973, 21: 1154-1157
    [16] Ben-Tal A, Nemirovski A. Robust convex optimization. Mathematics of Operations Research, 1998, 23(4):769-805
    [17] Bandi C, Bertsimas D. Tracable stochastic analysis in high dimensions via robust optimization. Mathematical Programming, 2012, 134(1): 23-70
    [18] Wang Le-Yi, Zhao Wen-Xiao. System identification: new paradigms, challenges, and opportunities. Acta AutomaticaSinica,2013, 39(7): 933-942(王乐一, 赵文虓. 系统辨识: 新的模式、挑战及机遇. 自动化学报, 2013,39(7): 933-942)
    [19] Ben-Tal A, Nemirovski A. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming, 2000, 88(3): 411-424
    [20] Ben-Tal A, Nemirovski A. Robust solutions of uncertain linear programs. Operations Research Letters, 1999, 25:1-13
    [21] El-Ghaoui L, Lebret H. Robust solutions to least-squares problems with uncertain data. SIAM Journal on Matrix Analysis and Applications, 1997, 18: 1035-1064
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出版历程
  • 收稿日期:  2013-04-08
  • 修回日期:  2013-08-12
  • 刊出日期:  2014-05-20

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