Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities

Harish J. Palanthandalam-Madapusi; Jesse B. Hoagg; Dennis S. Bernstein

doi:10.1109/ACC.2004.182709

Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities

Harish J. Palanthandalam-Madapusi, Jesse B. Hoagg, Dennis S. Bernstein

Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

For nonlinear systems with measured-input non-linearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selective-refinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the system nonlinearity. For both methods, polynomials, splines, sigmoids, wavelets, sines and cosines, or radial basis functions can be used as basis functions. Both approaches can be used to identify nonlinear maps with multiple arguments and with multiple outputs.

Original language	English
Title of host publication	Proceedings of the 2004 American Control Conference (AAC)
Pages	4788-4793
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2004.182709
State	Published - 2004
Event	Proceedings of the 2004 American Control Conference (AAC) - Boston, MA, United States Duration: Jun 30 2004 → Jul 2 2004

Publication series

Name	Proceedings of the American Control Conference
Volume	5
ISSN (Print)	0743-1619

Conference

Conference	Proceedings of the 2004 American Control Conference (AAC)
Country/Territory	United States
City	Boston, MA
Period	6/30/04 → 7/2/04

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2004.182709

Cite this

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title = "Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities",

abstract = "For nonlinear systems with measured-input non-linearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selective-refinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the system nonlinearity. For both methods, polynomials, splines, sigmoids, wavelets, sines and cosines, or radial basis functions can be used as basis functions. Both approaches can be used to identify nonlinear maps with multiple arguments and with multiple outputs.",

author = "Palanthandalam-Madapusi, {Harish J.} and Hoagg, {Jesse B.} and Bernstein, {Dennis S.}",

year = "2004",

doi = "10.1109/ACC.2004.182709",

language = "English",

isbn = "0780383354",

series = "Proceedings of the American Control Conference",

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booktitle = "Proceedings of the 2004 American Control Conference (AAC)",

note = "Proceedings of the 2004 American Control Conference (AAC) ; Conference date: 30-06-2004 Through 02-07-2004",

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Palanthandalam-Madapusi, HJ, Hoagg, JB & Bernstein, DS 2004, Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities. in Proceedings of the 2004 American Control Conference (AAC). Proceedings of the American Control Conference, vol. 5, pp. 4788-4793, Proceedings of the 2004 American Control Conference (AAC), Boston, MA, United States, 6/30/04. https://doi.org/10.1109/ACC.2004.182709

Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities. / Palanthandalam-Madapusi, Harish J.; Hoagg, Jesse B.; Bernstein, Dennis S.
Proceedings of the 2004 American Control Conference (AAC). 2004. p. 4788-4793 (Proceedings of the American Control Conference; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hoagg, Jesse B.

AU - Bernstein, Dennis S.

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N2 - For nonlinear systems with measured-input non-linearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selective-refinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the system nonlinearity. For both methods, polynomials, splines, sigmoids, wavelets, sines and cosines, or radial basis functions can be used as basis functions. Both approaches can be used to identify nonlinear maps with multiple arguments and with multiple outputs.

AB - For nonlinear systems with measured-input non-linearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selective-refinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the system nonlinearity. For both methods, polynomials, splines, sigmoids, wavelets, sines and cosines, or radial basis functions can be used as basis functions. Both approaches can be used to identify nonlinear maps with multiple arguments and with multiple outputs.

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Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities

Abstract

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