期刊文献+

基于正交投影的滚动窗口递推闭环子空间辨识 被引量:1

Scroll-Window Recursive Subspace Identification Methods for Closed-loop System Based on Orthogonal Projection
原文传递
导出
摘要 提出了一种基于正交投影的滚动窗口递推闭环子空间的辨识算法.它引入正交投影(QR)分解来进行正交投影、特征值分解,从而实现奇异值分解,并使用了bona fide递推算法来更新QR分解.然后根据投影变量选取原则选用了两个不同的投影变量W1和W2,推导并提出了基于正交投影的两种递推闭环子空间辨识方法.最后,数值仿真表明:提出的递推闭环子空间辨识方法是有效的、可行的. A novel scroll-window recursive subspaee identification algorithm for a closed-loop system is proposed based on orthogonal projection (Qlt) , which introduces QR decomposition to implement the QR and eigenval- ue decompositon and achieve a singular value deeomposition. In addition, a bona fide reeursive algorithm is used to update Ihe QR decomposition. Then, two different projection variables W1 and W2 are considered ac- cording to the selection principle, and two recursive subspace identification algorithms based on QR are de- rived and proposed. Finally, the numerical simulation results show that the proposed method is ettieient and feasible.
出处 《信息与控制》 CSCD 北大核心 2014年第1期56-62,共7页 Information and Control
基金 上海市科委重点资助项目(08160512100)
关键词 子空间辨识方法 正交投影 奇异值分解 subspace idenlification me-thod (SIM) orthogon'al projection (QR) singular value decomposition(SVD)
  • 相关文献

参考文献16

  • 1van Overschee P, de Moor B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems[J]. Automatica, 1994, 30(1): 75-93.
  • 2Larimore W E. Canonical variate analysis in identification, filtering and adaptive control [ C ]//Proceedings of the 29th Conference on Decision and Control. Piscataway, NJ, USA: IEEE, 1990 : 596 -604.
  • 3Verhaegen M, Dewilde P. Subspace model identification, Part I: The output-error state-space model identification class of algorithms [ J ]. Inter- national Journal of Control, 1992, 56(6) : 1187 -1210.
  • 4Wang J, Qin S J. A new subspace identification approach based on principal component analysis [ J]. Process Control, 2002, 12 (8) : 841 - 855.
  • 5Bauer D. Asymptotic properties of subspace estimators[ J]. Automatica, 2005, 41 (3) : 359 -376.
  • 6Gustafsson T. Recursive system identification using instrumental variable subspace tracking[ C 1//Proceedings of IFAC SYSID'97. Berlin, Ger- many: IFAC, 1997:1683-1688.
  • 7Mercere G, Bako L, Lecoeuche S. Propagator-based methods for recursive subspaee model identification[ Jl. Signal Processing, 2008, 88 (3) : 468 - 491.
  • 8姜月萍,方海涛.基于主成份分析的递推子空间辨识[J].系统科学与数学,2007,27(3):387-400. 被引量:2
  • 9杨华,李少远.一种新的基于遗忘因子的递推子空间辨识算法[J].控制理论与应用,2009,26(1):69-72. 被引量:28
  • 10黄金峰,张合新,胡友涛,张植.基于有限记忆变遗忘因子的子空间辨识算法[J].控制理论与应用,2012,29(7):893-898. 被引量:5

二级参考文献50

  • 1MERCERE G, LECOEUCHE S, LOVERA M. Recursive subspace identification based on instrumental variable unconstrained quadratic optimization[J]. International Journal of Adapt Control Signal Process, 2004, 18(4): 771 - 797.
  • 2JIANG Y, FANG H. Recursive subspace identification based on principal component analysis[C]//Proceedings of the 25th Chinese Control Conference. Harbin, China: [s.n.], 2006.
  • 3LOVERA M, GUSTAFSSON T, VERHAEGEN M. Recursive subspace identification of linear and non-linear Wiener state-space models[J]. Automatica, 2000, 36(11): 1639- 1650.
  • 4OKU H, KIMURA H. Recursive 4SID algorithms using gradient type subspace tracking[J]. Automatica, 2002, 38(6): 1035 - 1043.
  • 5ZHANG C, BITMEAD R R. Subspace system identification for training-based MIMO channel estimation[J]. Automatica, 2005, 41(9): 1623 - 1632.
  • 6YANG B. Asymptotic convergence analysis of the projection approximation subspace tracking algorithms[J]. Signal Processing, 1996, 50(1/2): 123- 136.
  • 7张贤达.矩阵分析与应用[M].北京:清华大学出版社,2005.341-400.
  • 8Bauer D. Asympotic properties of subspace estimators. Automatica, 2005, 41: 359-376.
  • 9Chui N and Maciejowski J M. Criteria for informative experiments with subspace identification. International Journal of Control, 2005, 78(5): 326-344.
  • 10Mercere G, Lecoeuche S and Vasseur C. Sequential correlation based propagator algorithm for recursive subspace identification. Prague, 2005 IFAC.

共引文献33

同被引文献18

  • 1薛振框,李少远.基于模型有效匹配的多模型切换控制[J].上海交通大学学报,2005,39(3):353-356. 被引量:4
  • 2朱豫才.过程控制的多变量系统辨识[M].长沙:国防科技大学出版社,2005.
  • 3Qin S J, Badgwell T A. A survey of industrial model predictive control technology [ J ]. Control Engineering Practice, 2003, 11 (7) . 733 - 764.
  • 4Zhu Y C, Xu Z H, Zhao J, et al. Development and application of an integrated MPC technology [ C ]//Proceedings of the 17th IFAC World Congress. San Diego, USA. IFAC, 2008 . 6962 - 6967.
  • 5Zhu Y C, Patwardhan R, Wagner S B, et al. Towards a low cost and high performance MPC . The role of system identification[ J ]. Computers & Chemical Engineering, 2013, 51 ( 1 ) . 124 - 135.
  • 6Richalet J. Industrial applications of model based predictive control[ J ]. Automatica, 1993, 29 (5) . 1251 - 1274.
  • 7Zhu Y C. Multivariable process identification for MPC. The asymptotic method and its applications [ J ]. Journal of Process Control, 1998, 8 (2). 101-115.
  • 8Akpan V A, Hassapis G D. Nonlinear model identification and adaptive model predictive control using neural networks [ J]. ISA Transactions, 2011, 50(2) . 177 -194.
  • 9Aggelogiannaki E, Doganis P, Sarimveis H. An adaptive model predictive control configuration for production-inventory systems[ J]. Interna- tional Journal of Production Economics, 2008, 114( 1 ) . 165 - 178.
  • 10Karra S, Shaw R, Patwardhan S C, et al. Adaptive model predictive control of muhivariable time-varying systems[ J]. Industrial and Engineer- ing Chemistry Research, 2008, 47 (8) . 2708 - 2720.

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部