On behavioral interpolation in local LPV system identification

P. den Boef, R. Tóth, M. Schoukens

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Abstract

State-space identification of Linear Parameter-Varying (LPV) models using local data still represents a significant challenge as interpolation of local state-space models suffers from the well-known state-basis coherence problem. Recently, various behavioral-type of interpolation methods, in which a global LPV model is constructed based on matching its input-output behavior with the local models, have been introduced to overcome this issue. However, these methods suffer from high computational complexity and stability restrictions of the local models. In this paper, a novel method is introduced that is based on direct local-matrix norm matching, which, contrary to previous works, does not suffer from basis incoherence of the local models and neither requires their stability. Although these properties are highly desirable, a simulation study on an e-Nose sensor system indicates that the method in its current form is not robust to noise and therefore future research is needed to apply the presented concept in a realistic system identification setting.
Original languageEnglish
Pages (from-to)20-25
JournalIFAC-PapersOnLine
Volume52
Issue number28
DOIs
Publication statusPublished - 4 Nov 2019
Event3rd IFAC Workshop on Linear Parameter-Varying Systems - Eindhoven, Netherlands
Duration: 4 Nov 20196 Nov 2019

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Identification (control systems)
Interpolation
Computational complexity
Sensors

Bibliographical note

Part of special issue:
3rd IFAC Workshop on Linear Parameter Varying Systems LPVS 2019: Eindhoven, Netherlands, 4–6 November 2019

Cite this

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abstract = "State-space identification of Linear Parameter-Varying (LPV) models using local data still represents a significant challenge as interpolation of local state-space models suffers from the well-known state-basis coherence problem. Recently, various behavioral-type of interpolation methods, in which a global LPV model is constructed based on matching its input-output behavior with the local models, have been introduced to overcome this issue. However, these methods suffer from high computational complexity and stability restrictions of the local models. In this paper, a novel method is introduced that is based on direct local-matrix norm matching, which, contrary to previous works, does not suffer from basis incoherence of the local models and neither requires their stability. Although these properties are highly desirable, a simulation study on an e-Nose sensor system indicates that the method in its current form is not robust to noise and therefore future research is needed to apply the presented concept in a realistic system identification setting.",
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On behavioral interpolation in local LPV system identification. / den Boef, P.; Tóth, R.; Schoukens, M.

In: IFAC-PapersOnLine, Vol. 52, No. 28, 04.11.2019, p. 20-25.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - den Boef, P.

AU - Tóth, R.

AU - Schoukens, M.

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AB - State-space identification of Linear Parameter-Varying (LPV) models using local data still represents a significant challenge as interpolation of local state-space models suffers from the well-known state-basis coherence problem. Recently, various behavioral-type of interpolation methods, in which a global LPV model is constructed based on matching its input-output behavior with the local models, have been introduced to overcome this issue. However, these methods suffer from high computational complexity and stability restrictions of the local models. In this paper, a novel method is introduced that is based on direct local-matrix norm matching, which, contrary to previous works, does not suffer from basis incoherence of the local models and neither requires their stability. Although these properties are highly desirable, a simulation study on an e-Nose sensor system indicates that the method in its current form is not robust to noise and therefore future research is needed to apply the presented concept in a realistic system identification setting.

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