Identification of dominant subspaces for model reduction of structured parametric systems. (English) Zbl 1542.93049
Summary: In this paper, we discuss a novel model reduction framework for linear structured dynamical systems. The transfer functions of these systems are assumed to have a special structure, for example, coming from second-order linear systems or time-delay systems, and they may also have parameter dependencies. Firstly, we investigate the connection between classic interpolation-based model reduction methods with the reachability and observability subspaces of linear structured parametric systems. We show that if enough interpolation points are taken, the projection matrices of interpolation-based model reduction encode these subspaces. Consequently, we are able to identify the dominant reachable and observable subspaces of the underlying system. Based on this, we propose a new model reduction algorithm combining these features and leading to reduced-order systems. Furthermore, we discuss computational aspects of the approach and its applicability to a large-scale setting. We illustrate the efficiency of the proposed approach with several numerical large-scale benchmark examples.
© 2024 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd.
© 2024 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd.
MSC:
| 93B11 | System structure simplification |
| 93C05 | Linear systems in control theory |
| 93B03 | Attainable sets, reachability |
| 93B07 | Observability |
| 93B05 | Controllability |
Keywords:
controllability and observability; interpolation; linear structured systems; model order reduction; parametric systemsReferences:
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