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Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-6490-1885
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-8457-4105
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2021 (English)In: Proceedings The 60th IEEE conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper, Published paper (Refereed)
Abstract [en]

Differential-algebraic equations (DAEs) arise naturally as a result of equation-based object-oriented modeling. In many cases, these models contain unknown parameters that have to be estimated using experimental data. However, often the system is subject to unknown disturbances which, if not taken into account in the estimation, can severely affect the model's accuracy. For non-linear state-space models, particle filter methods have been developed to tackle this issue. Unfortunately, applying such methods to non-linear DAEs requires a transformation into a state-space form, which is particularly difficult to obtain for models with process disturbances. In this paper, we propose a simulation-based prediction error method that can be used for non-linear DAEs where disturbances are modeled as continuous-time stochastic processes. To the authors' best knowledge, there are no general methods successfully dealing with parameter estimation for this type of model. One of the challenges in particle filtering  methods are random variations in the minimized cost function due to the nature of the algorithm. In our approach, a similar phenomenon occurs and we explicitly consider how to sample the underlying continuous process to mitigate this problem. The method is illustrated numerically on a pendulum example. The results suggest that the method is able to deliver consistent estimates.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021.
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords [en]
Nonlinear Identification; Process Disturbance; Differential-Algebraic Equations; Parameter Estimation; Simulated PEM
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-292187DOI: 10.1109/CDC45484.2021.9683787ISI: 000781990302018Scopus ID: 2-s2.0-85126001444OAI: oai:DiVA.org:kth-292187DiVA, id: diva2:1539989
Conference
The 60th IEEE conference on Decision and Control (CDC), Dec. 13-17, 2021, Austin, Texas, USA
Funder
Swedish Research Council, 2019-04956 and 2016-06079 (the research environment NewLEADS)Swedish Foundation for Strategic Research, FFL15-0032
Note

Financially also supported by SRA ICT TNG (Digital Futures) and KTH.

Part of proceedings ISBN 978-1-6654-3659-5

QC 20210326

QC 20220705

Available from: 2021-03-26 Created: 2021-03-26 Last updated: 2022-07-05Bibliographically approved

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Abdalmoaty, MohamedEriksson, OscarBereza-Jarocinski, RobertBroman, DavidHjalmarsson, Håkan

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