|Publisher DOI:||10.1109/CDC.2017.8264184||Title:||LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods||Language:||English||Authors:||Schulz, Erik
Cox, Pepijn B.
|Issue Date:||18-Jan-2018||Source:||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (2018-January): 3575-3581 (2018-01-18)||Abstract (english):||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.||Conference:||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017||URI:||http://hdl.handle.net/11420/2485||ISBN:||978-150902873-3||Institute:||Regelungstechnik E-14||Type:||InProceedings (Aufsatz / Paper einer Konferenz etc.)|
|Appears in Collections:||Publications without fulltext|
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