Passivity-Preserving Balanced Truncation Model Reduction of Circuit Equations

2010 ◽  
pp. 483-490 ◽  
Author(s):  
Tatjana Stykel ◽  
Timo Reis
Keyword(s):  
Model Reduction ◽  
Balanced Truncation

Model reduction in power systems using a structure-preserving balanced truncation approach

2019 ◽  
Vol 177 ◽  
pp. 106002
Author(s):  
Johnny Leung ◽  
Michel Kinnaert ◽  
Jean-Claude Maun ◽  
Fortunato Villella
Keyword(s):  
Power Systems ◽  
Model Reduction ◽  
Balanced Truncation ◽  
Structure Preserving

scholarly journals Model reduction by balanced truncation of dominant Lure systems

2020 ◽  
Vol 53 (2) ◽  
pp. 5617-5622
Author(s):  
Alberto Padoan ◽  
Fulvio Forni ◽  
Rodolphe Sepulchre
Keyword(s):  
Model Reduction ◽  
Balanced Truncation

scholarly journals Balanced truncation model reduction for descriptor systems

PAMM ◽  
2003 ◽  
Vol 3 (1) ◽  
pp. 5-8
Author(s):  
Tatjana Stykel
Keyword(s):  
Model Reduction ◽  
Descriptor Systems ◽  
Balanced Truncation

scholarly journals Empirical Reduced-Order Modeling for Boundary Feedback Flow Control

2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
Seddik M. Djouadi ◽  
R. Chris Camphouse ◽  
James H. Myatt
Keyword(s):  
Model Reduction ◽  
Reduction Method ◽  
Linear Subspace ◽  
Balanced Truncation ◽  
Order Model ◽  
Reduced Order ◽  
Domain Identification ◽  
Controllability And Observability ◽  
Obstacle Geometry ◽  
Proper Orthogonal

This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. Various aspects of model reduction are discussed that apply to partial differential equation- (PDE-) based models in general. Specifically, the proper orthogonal decomposition (POD) of a high dimension system as well as frequency domain identification methods are discussed for initial model construction. Projections on the POD basis give a nonlinear Galerkin model. Then, a model reduction method based on empirical balanced truncation is developed and applied to the Galerkin model. The rationale for doing so is that linear subspace approximations to exact submanifolds associated with nonlinear controllability and observability require only standard matrix manipulations utilizing simulation/experimental data. The proposed method uses a chirp signal as input to produce the output in the eigensystem realization algorithm (ERA). This method estimates the system's Markov parameters that accurately reproduce the output. Balanced truncation is used to show that model reduction is still effective on ERA produced approximated systems. The method is applied to a prototype convective flow on obstacle geometry. AnH∞feedback flow controller is designed based on the reduced model to achieve tracking and then applied to the full-order model with excellent performance.

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