LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods

Publikationstyp
Conference Paper
Date Issued
2017
Sprache
English
Author(s)
Schulz, Erik  orcid-logo
Cox, Pepijn B.  
Toth, Roland  
Werner, Herbert  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/2485
Start Page
3575
End Page
3581
Citation
IEEE 56th Annual Conference on Decision and Control (CDC 2017)
Contribution to Conference
56th IEEE Annual Conference on Decision and Control, CDC 2017  
Publisher DOI
10.1109/CDC.2017.8264184
Scopus ID
2-s2.0-85046148897
In this paper, we introduce a procedure for global identification of linear parameter-varying (LPV) discrete-time state-space (SS) models with a static, affine dependency structure in a computationally efficient way. The aim is to develop off-the-shelf LPV-SS estimation methods to make identification practically accessible. The benefits of identifying a computational straightforward LPV input-output (IO) model-that has an equivalent SS representation with static, affine dependency-is combined with an LPV-SS model order reduction. To increase practical relevance of the proposed scheme, in this paper, we present a computational attractive model order reduction scheme based on the LPV Ho-Kalman like realization scheme. We analyze the computational complexity and scalability of our method and compare its benefits to the PBSIDopt scheme. Two examples are provided to demonstrate that our introduced approach performs similar to PBSIDopt in a numerical example and outperforms the PBSIDopt on measurements of a real world system, the air-path system of a gasoline engine.