Partial Fraction Expansion Based Frequency Weighted Balanced Singular Perturbation Approximation Model Reduction Technique with Error Bounds

详细信息    查看全文

• 作者：Deepak Kumar^a ; ^{deepak_kumar@mnnit.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Ahmad Jazlan^b ; ^c ; ^{ahmadjazlan@iium.edu.my" class="auth_mail" title="E-mail the corresponding author} ; Victor Sreeram^b ; ^{victor.sreeram@uwa.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Roberto Togneri^b ; ^{roberto.togneri@uwa.edu.au" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Model Reduction ; Frequency Weighting ; Singular Perturbation Approximation ; Partial Fraction Expansion ; Square Root Balancing
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：9
• 页码：45-50
• 全文大小：502 K

文摘

In this paper, a new frequency weighted partial fraction expansion based model reduction technique is developed based on the partial fraction expansion approach. In order to further reduce the frequency weighted approximation error, singular perturbation approximation is incorporated into the algorithm. This technique results in stable reduced order models regardless if single sided or double sided weights are used. Error bounds are also derived for the proposed method. For minimization of the frequency weighted approximation error, free parameters are introduced into the algorithm. A numerical example is provided in order to validate the proposed algorithm.