D-Optimal Design for Information Driven Identification of Static Nonlinear Elements

Ulapane, N; Thiyagarajan, K; Kodagoda, S; Nguyen, L

D-Optimal Design for Information Driven Identification of Static Nonlinear Elements

Ulapane, N Thiyagarajan, K

Kodagoda, S

Nguyen, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 492-497
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Ulapane, N
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.contributor.author	Nguyen, L
dc.date	2021-08-01
dc.date.accessioned	2021-12-27T10:27:14Z
dc.date.available	2021-12-27T10:27:14Z
dc.date.issued	2021-08-01
dc.identifier.citation	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 492-497
dc.identifier.isbn	9781665422482
dc.identifier.uri	http://hdl.handle.net/10453/152528
dc.description.abstract	Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications
dc.relation.isbasedon	10.1109/iciea51954.2021.9516333
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	D-Optimal Design for Information Driven Identification of Static Nonlinear Elements
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Chengdu, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-08-30T00:00:00+1000Z
dc.date.updated	2021-12-27T10:27:13Z
pubs.finish-date	2021-08-04
pubs.publication-status	Published
pubs.start-date	2021-08-01
pubs.volume	00

Abstract:

Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well.

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http://hdl.handle.net/10453/152528