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LPV System Local Model Interpolation Based on Combined Model Reduction

Qinghua Zhang 1 
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Résumé : The local approach to linear parameter varying (LPV) system identification consists in interpolating a collection of linear time invariant (LTI) models, which have been estimated from data acquired at different working points of a nonlinear system. Interpolation is essential in this approach. When the local LTI models are in state-space form, as each local model can be estimated with an arbitrary state basis, it is widely acknowledged that the local models should be made coherent before their interpolation. In order to avoid the delicate task of making local state-space models coherent, a new interpolation method of local state-space models is proposed in this paper, which does not require coherent local models. This method is based on the reduction of the large state-space model built by combining the local models. Numerical examples are presented to illustrate the effectiveness of this method.
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Submitted on : Wednesday, October 31, 2018 - 11:30:01 AM
Last modification on : Friday, June 17, 2022 - 1:27:36 PM
Long-term archiving on: : Friday, February 1, 2019 - 1:06:30 PM


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  • HAL Id : hal-01909552, version 1



Qinghua Zhang. LPV System Local Model Interpolation Based on Combined Model Reduction. SYSID 2018, 18th IFAC Symposium on System Identification, Jul 2018, Stockholm, France. ⟨hal-01909552⟩



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