Moving horizon estimation for nonlinear systems with time-varying parameters

Publikation: Beitrag in FachzeitschriftKonferenzaufsatz in FachzeitschriftForschungPeer-Review

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OriginalspracheEnglisch
Seiten (von - bis)341-348
Seitenumfang8
FachzeitschriftIFAC-PapersOnLine
Jahrgang58
Ausgabenummer18
Frühes Online-Datum25 Sept. 2024
PublikationsstatusVeröffentlicht - 2024
Veranstaltung8th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2024 - Kyoto, Japan
Dauer: 21 Aug. 202424 Aug. 2024

Abstract

We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensure boundedness of the estimation error (with respect to the disturbance energy). The proposed estimation scheme involves a standard quadratic cost function with an adaptive regularization term depending on the current parameter observability. We develop robustness guarantees for the overall estimation error that are valid for all times, and that improve the more often the parameters are detected to be observable during operation. The theoretical results are illustrated by a simulation example.

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Moving horizon estimation for nonlinear systems with time-varying parameters. / Schiller, Julian D.; Müller, Matthias A.
in: IFAC-PapersOnLine, Jahrgang 58, Nr. 18, 2024, S. 341-348.

Publikation: Beitrag in FachzeitschriftKonferenzaufsatz in FachzeitschriftForschungPeer-Review

Schiller JD, Müller MA. Moving horizon estimation for nonlinear systems with time-varying parameters. IFAC-PapersOnLine. 2024;58(18):341-348. Epub 2024 Sep 25. doi: 10.1016/j.ifacol.2024.09.053
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AU - Schiller, Julian D.

AU - Müller, Matthias A.

N1 - Publisher Copyright: Copyright © 2024 The Authors.

PY - 2024

Y1 - 2024

N2 - We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensure boundedness of the estimation error (with respect to the disturbance energy). The proposed estimation scheme involves a standard quadratic cost function with an adaptive regularization term depending on the current parameter observability. We develop robustness guarantees for the overall estimation error that are valid for all times, and that improve the more often the parameters are detected to be observable during operation. The theoretical results are illustrated by a simulation example.

AB - We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensure boundedness of the estimation error (with respect to the disturbance energy). The proposed estimation scheme involves a standard quadratic cost function with an adaptive regularization term depending on the current parameter observability. We develop robustness guarantees for the overall estimation error that are valid for all times, and that improve the more often the parameters are detected to be observable during operation. The theoretical results are illustrated by a simulation example.

KW - Moving horizon estimation

KW - parameter estimation

KW - state estimation

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DO - 10.1016/j.ifacol.2024.09.053

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SP - 341

EP - 348

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

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T2 - 8th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2024

Y2 - 21 August 2024 through 24 August 2024

ER -

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