Abstract
A novel model reduction methodology is proposed to approximate large-scale nonlinear dynamical systems. The methodology amounts to finding computationally efficient substitute models for the nonlinear subsystems. Model reduction is pursued by viewing the system as a grey-box (or hybrid) model with a mechanistic (white-box) component and an empirical (black-box) component. Before identifying the substitute model, the mechanistic subsystem is reduced by projection using proper orthogonal decomposition. Subsequently, the empirical component is identified by parameter estimation to substitute the nonlinear subsystem. As a consequence, a reduced model with less nonlinear complexity is obtained. An example involving a distributed parameter system is provided.
Original language | English |
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Pages (from-to) | 906-914 |
Number of pages | 9 |
Journal | Journal of Process Control |
Volume | 18 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Oct 2008 |
Funding
This work has been supported by the European Union within the Marie-Curie Training Network PROMATCH under the Grant No. MRTN-CT-2004-512441.
Keywords
- Distributed parameter systems
- Grey-box modeling
- Hybrid modeling
- Model reduction
- Parameter estimation
- Proper orthogonal decomposition