A posteriori error bounds for discrete balanced truncation

被引:4
作者
Chahlaoui, Y. [1 ]
机构
[1] Univ Manchester, CICADA, Sch Math, Manchester M13 9PL, Lancs, England
基金
英国工程与自然科学研究理事会;
关键词
Model reduction; Balanced truncation; A posteriori error bound; Gramians; Stein equations; H-2-norm; KRYLOV-SUBSPACE METHODS; MODEL-REDUCTION; SYSTEMS;
D O I
10.1016/j.laa.2011.07.025
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Balanced truncation of discrete linear time-invariant systems is an automatic method once an error tolerance is specified and it yields an a priori error bound, which is why it is widely used in engineering for simulation and control. We derive a discrete version of Antoulas's H-2-norm error formula and show how to adapt it to some special cases. We present an a posteriori computable upper bound for the H-2-norm of the error system defined as the system whose transfer function corresponds to the difference between the transfer function of the original system and the transfer function of the reduced system. We also present a generalization of the H-2-norm error formula to any projection of dynamics method. The main advantage of our results is that we use the information already available in the model reduction algorithm in order to compute the H-2-norm instead of computing a new Gramian of the corresponding error system, which is computationally expensive. The a posteriori bound gives insight into the quality of the reduced system and it can be used to solve many problems accompanying the order reduction operation. Moreover, it is often more accurate in floating point arithmetic. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:2744 / 2763
页数:20
相关论文
共 50 条
[41]   POSITIVITY PRESERVING BALANCED TRUNCATION FOR DESCRIPTOR SYSTEMS [J].
Reis, Timo ;
Virnik, Elena .
SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2009, 48 (04) :2600-2619
[42]   A tangential method for the balanced truncation in model reduction [J].
Y. Kaouane .
Numerical Algorithms, 2020, 83 :629-652
[43]   Balanced truncation for model reduction of biological oscillators [J].
Alberto Padoan ;
Fulvio Forni ;
Rodolphe Sepulchre .
Biological Cybernetics, 2021, 115 :383-395
[44]   An Improved Algorithm for Frequency Weighted Balanced Truncation [J].
Muda, Wan Mariam Wan ;
Sreeram, Victor ;
Iu, Herbert Ho-Ching .
2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, :7182-7187
[45]   Balanced truncation of networked linear passive systems [J].
Cheng, Xiaodong ;
Scherpen, Jacquelien M. A. ;
Besselink, Bart .
AUTOMATICA, 2019, 104 :17-25
[46]   Dimension Reduction and Controller Design for Large Scale Systems Using Balanced Truncation [J].
Maurya, Manoj Kumar ;
Kumar, Awadhesh .
2017 1ST INTERNATIONAL CONFERENCE ON ELECTRONICS, MATERIALS ENGINEERING & NANO-TECHNOLOGY (IEMENTECH), 2017,
[47]   Balanced truncation model reduction for semidiscretized Stokes equation [J].
Stykel, Tatjana .
LINEAR ALGEBRA AND ITS APPLICATIONS, 2006, 415 (2-3) :262-289
[48]   Rigorous and effective a-posteriori error bounds for nonlinear problems-application to RB methods [J].
Schmidt, Andreas ;
Wittwar, Dominik ;
Haasdonk, Bernard .
ADVANCES IN COMPUTATIONAL MATHEMATICS, 2020, 46 (02)
[49]   Balanced truncation of linear time-varying systems [J].
Sandberg, H ;
Rantzer, A .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2004, 49 (02) :217-229
[50]   Laguerre-Based Low-Rank Balanced Truncation of Discrete-Time Systems [J].
Xiao, Zhi-Hua ;
Fang, Ya-Xin ;
Jiang, Yao-Lin .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2023, 70 (08) :3014-3018