scholarly journals Input-Tailored System-Theoretic Model Order Reduction for Quadratic-Bilinear Systems

2022 ◽  
Vol 43 (1) ◽  
pp. 1-39
Author(s):  
Björn Liljegren-Sailer ◽  
Nicole Marheineke
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Bilinear Systems ◽  
Theoretic Model ◽  
Model Order

scholarly journals System‐theoretic model order reduction with pyMOR

PAMM ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Linus Balicki ◽  
Petar Mlinarić ◽  
Stephan Rave ◽  
Jens Saak
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Theoretic Model ◽  
Model Order

scholarly journals The Versatile Cross Gramian for System Theoretic Model Reduction and More

2015 ◽  
Author(s):  
Christian Himpe ◽  
Mario Ohlberger
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Theoretic Model ◽  
Model Order ◽  
Gramian Matrix ◽  
The Cross ◽  
Analysis System ◽  
Single Matrix ◽  
Controllability And Observability ◽  
Symmetric Systems

For input-output systems, the cross gramian matrix encodes controllability and observability information into a single matrix, which are essential to system-theoretic applications. This system gramian can be used, in example, for model order reduction, sensitivity analysis, system identification, decentralized control and parameter identification. Beyond linear symmetric systems, the cross gramian is also available for parametric, non-symmetric, non-square and nonlinear systems.

scholarly journals Interpolatory model reduction for quadratic-bilinear systems using error estimators

2018 ◽  
Vol 36 (1) ◽  
pp. 25-44 ◽  
Author(s):  
Mian Ilyas Ahmad ◽  
Peter Benner ◽  
Lihong Feng
Keyword(s):  
Model Reduction ◽  
Error Bound ◽  
Model Order Reduction ◽  
Computational Cost ◽  
Order Reduction ◽  
Bilinear Systems ◽  
Descriptor System ◽  
Model Order ◽  
Content Type ◽  
Projection Matrices

Purpose The purpose of this paper is to propose an interpolation-based projection framework for model reduction of quadratic-bilinear systems. The approach constructs projection matrices from the bilinear part of the original quadratic-bilinear descriptor system and uses these matrices to project the original system. Design/methodology/approach The projection matrices are constructed by viewing the bilinear system as a linear parametric system, where the input associated with the bilinear part is treated as a parameter. The advantage of this approach is that the projection matrices can be constructed reliably by using an a posteriori error bound for linear parametric systems. The use of the error bound allows us to select a good choice of interpolation points and parameter samples for the construction of the projection matrices by using a greedy-type framework. Findings The results are compared with the standard quadratic-bilinear projection methods and it is observed that the approximations through the proposed method are comparable to the standard method but at a lower computational cost (offline time). Originality/value In addition to the proposed model order reduction framework, the authors extend the one-sided moment matching parametric model order reduction (PMOR) method to a two-sided method that doubles the number of moments matched in the PMOR method.

Sign in / Sign up

Export Citation Format

Share Document