Zhu, Y.-P. and Lang, Z.-Q. (2020) A new convergence analysis for the Volterra series representation of nonlinear systems. Automatica, 111. ISSN 0005-1098
Abstract
The convergence of the Volterra series representation of nonlinear systems is the fundamental requirement for the analysis of nonlinear systems in the frequency domain. In the present study, a new criterion is derived to determine the convergence of the Volterra series representation of nonlinear systems described by a NARX (Nonlinear Auto Regressive with eXegenous input) model. The analysis is performed based on a new function known as Generalized Output Bound Characteristic Function (GOBCF), which is defined in terms of the input, output and parameters of the NARX model of nonlinear systems. Compared to the existing results, the new criterion provides a much more rigorous and effective approach to the analysis of the convergence conditions and properties of the Volterra series representation of nonlinear systems. Two case studies have been used to demonstrate the effectiveness of the new convergence analysis criterion and the advantages of the new analysis over those produced by existing approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier. This is an author produced version of a paper subsequently published in Automatica. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Volterra series; NARX model; Nonlinear systems; Convergence criterion |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Sep 2019 14:58 |
Last Modified: | 11 Oct 2020 00:38 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.automatica.2019.108599 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150619 |
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Filename: Automatica 18-0566 final Manuscript.pdf
Licence: CC-BY-NC-ND 4.0