Structure-Preserving Model Reduction for Nonlinear Port-Hamiltonian Systems
Authors
S. Chaturantabut, C. Beattie, S. Gugercin
Abstract
This paper presents a structure-preserving model reduction approach applicable to large-scale, nonlinear port-Hamiltonian systems. Structure preservation in the reduction step ensures the retention of port-Hamiltonian structure which, in turn, assures the stability and passivity of the reduced model. Our analysis provides a priori error bounds for both state variables and outputs. Three techniques are considered for constructing bases needed for the reduction: one that utilizes proper orthogonal decompositions; one that utilizes \( \mathcal{H}2/\mathcal{H}{\infty} \)-derived optimized bases; and one that is a mixture of the two. The complexity of evaluating the reduced nonlinear term is managed efficiently using a modification of the discrete empirical interpolation method (DEIM) that also preserves port-Hamiltonian structure. The efficiency and accuracy of this model reduction framework are illustrated with two examples: a nonlinear ladder network and a tethered Toda lattice.
Citation
- Journal: SIAM Journal on Scientific Computing
- Year: 2016
- Volume: 38
- Issue: 5
- Pages: B837–B865
- Publisher: Society for Industrial & Applied Mathematics (SIAM)
- DOI: 10.1137/15m1055085
BibTeX
@article{Chaturantabut_2016,
title={{Structure-Preserving Model Reduction for Nonlinear Port-Hamiltonian Systems}},
volume={38},
ISSN={1095-7197},
DOI={10.1137/15m1055085},
number={5},
journal={SIAM Journal on Scientific Computing},
publisher={Society for Industrial & Applied Mathematics (SIAM)},
author={Chaturantabut, S. and Beattie, C. and Gugercin, S.},
year={2016},
pages={B837--B865}
}
References
- Astrid, P., Weiland, S., Willcox, K. & Backx, T. Missing Point Estimation in Models Described by Proper Orthogonal Decomposition. IEEE Transactions on Automatic Control vol. 53 2237–2251 (2008) – 10.1109/tac.2008.2006102
- Barrault, M., Maday, Y., Nguyen, N. C. & Patera, A. T. An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus. Mathématique vol. 339 667–672 (2004) – 10.1016/j.crma.2004.08.006
- Carlberg, K., Farhat, C., Cortial, J. & Amsallem, D. The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows. Journal of Computational Physics vol. 242 623–647 (2013) – 10.1016/j.jcp.2013.02.028
- Carlberg, K., Tuminaro, R. & Boggs, P. Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics. SIAM Journal on Scientific Computing vol. 37 B153–B184 (2015) – 10.1137/140959602
- Chaturantabut, S. & Sorensen, D. C. Nonlinear Model Reduction via Discrete Empirical Interpolation. SIAM Journal on Scientific Computing vol. 32 2737–2764 (2010) – 10.1137/090766498
- Drmač, Z. & Gugercin, S. A New Selection Operator for the Discrete Empirical Interpolation Method—Improved A Priori Error Bound and Extensions. SIAM Journal on Scientific Computing vol. 38 A631–A648 (2016) – 10.1137/15m1019271
- Everson, R. & Sirovich, L. Karhunen–Loève procedure for gappy data. Journal of the Optical Society of America A vol. 12 1657 (1995) – 10.1364/josaa.12.001657
- Fujimoto K., NY, IEEE (2007)
- Fujimoto, K. & Scherpen, J. M. A. Balanced Realization and Model Order Reduction for Nonlinear Systems Based on Singular Value Analysis. SIAM Journal on Control and Optimization vol. 48 4591–4623 (2010) – 10.1137/070695332
- Gugercin, S., Antoulas, A. C. & Beattie, C. $\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems. SIAM Journal on Matrix Analysis and Applications vol. 30 609–638 (2008) – 10.1137/060666123
- Gugercin S., People’s Republic of China (2009)
- Gugercin, S., Polyuga, R. V., Beattie, C. & van der Schaft, A. Structure-preserving tangential interpolation for model reduction of port-Hamiltonian systems. Automatica vol. 48 1963–1974 (2012) – 10.1016/j.automatica.2012.05.052
- Kunisch, K. & Volkwein, S. Galerkin proper orthogonal decomposition methods for parabolic problems. Numerische Mathematik vol. 90 117–148 (2001) – 10.1007/s002110100282
- Lumley J., Moscow (1967)
- Peherstorfer, B., Butnaru, D., Willcox, K. & Bungartz, H.-J. Localized Discrete Empirical Interpolation Method. SIAM Journal on Scientific Computing vol. 36 A168–A192 (2014) – 10.1137/130924408
- Peherstorfer, B. & Willcox, K. Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM Journal on Scientific Computing vol. 37 A2123–A2150 (2015) – 10.1137/140989169
- Phillips J., New York (2000)
- Polyuga, R. V. & van der Schaft, A. Structure preserving model reduction of port-Hamiltonian systems by moment matching at infinity. Automatica vol. 46 665–672 (2010) – 10.1016/j.automatica.2010.01.018
- Polyuga, R. V. & van der Schaft, A. Structure Preserving Moment Matching for Port-Hamiltonian Systems: Arnoldi and Lanczos. IEEE Transactions on Automatic Control vol. 56 1458–1462 (2011) – 10.1109/tac.2011.2128650
- Rewienski, M. & White, J. A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems vol. 22 155–170 (2003) – 10.1109/tcad.2002.806601
- Sirovich, L. Turbulence and the dynamics of coherent structures. I. Coherent structures. Quarterly of Applied Mathematics vol. 45 561–571 (1987) – 10.1090/qam/910462
- Söderlind, G. The logarithmic norm. History and modern theory. BIT Numerical Mathematics vol. 46 631–652 (2006) – 10.1007/s10543-006-0069-9
- Szyld, D. B. The many proofs of an identity on the norm of oblique projections. Numerical Algorithms vol. 42 309–323 (2006) – 10.1007/s11075-006-9046-2
- van der Schaft A., Zürich (2006)
- Willems, J. C. Dissipative dynamical systems part I: General theory. Archive for Rational Mechanics and Analysis vol. 45 321–351 (1972) – 10.1007/bf00276493