A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising

This paper presents a novel method to identify a class of closed-loop systems, in which both the forward channel and the feedback channel have unknown time-delays. Taking into account the time-delays, an overparameterized identification model with a sparse parameter vector is established. Based on t...

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Main Authors: Ying Chen, Yanjun Liu, Jing Chen, Junxia Ma
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9015976/
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spelling doaj-12bf89c7b08e4cbe966f6ad322d4b0c32021-03-30T02:32:14ZengIEEEIEEE Access2169-35362020-01-018996489965410.1109/ACCESS.2020.29768629015976A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-NoisingYing Chen0https://orcid.org/0000-0002-0376-0705Yanjun Liu1https://orcid.org/0000-0002-3086-3785Jing Chen2https://orcid.org/0000-0001-5615-2255Junxia Ma3https://orcid.org/0000-0002-0151-31881Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, China1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, China1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, ChinaThis paper presents a novel method to identify a class of closed-loop systems, in which both the forward channel and the feedback channel have unknown time-delays. Taking into account the time-delays, an overparameterized identification model with a sparse parameter vector is established. Based on the basis pursuit de-noising criterion, the sparse parameter vector is estimated by solving a quadratic programming. The time-delays and the parameters are estimated according to the structure of the parameter estimation vector and the model equivalence principle, respectively. The proposed method is applicable even in the case of a few number of sampled data. The effectiveness of the proposed algorithm is verified by the numerical simulation results.https://ieeexplore.ieee.org/document/9015976/Closed-loop systembasis pursuit de-noisingsystem identificationtime-delay estimation
collection DOAJ
language English
format Article
sources DOAJ
author Ying Chen
Yanjun Liu
Jing Chen
Junxia Ma
spellingShingle Ying Chen
Yanjun Liu
Jing Chen
Junxia Ma
A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
IEEE Access
Closed-loop system
basis pursuit de-noising
system identification
time-delay estimation
author_facet Ying Chen
Yanjun Liu
Jing Chen
Junxia Ma
author_sort Ying Chen
title A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
title_short A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
title_full A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
title_fullStr A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
title_full_unstemmed A Novel Identification Method for a Class of Closed-Loop Systems Based on Basis Pursuit De-Noising
title_sort novel identification method for a class of closed-loop systems based on basis pursuit de-noising
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description This paper presents a novel method to identify a class of closed-loop systems, in which both the forward channel and the feedback channel have unknown time-delays. Taking into account the time-delays, an overparameterized identification model with a sparse parameter vector is established. Based on the basis pursuit de-noising criterion, the sparse parameter vector is estimated by solving a quadratic programming. The time-delays and the parameters are estimated according to the structure of the parameter estimation vector and the model equivalence principle, respectively. The proposed method is applicable even in the case of a few number of sampled data. The effectiveness of the proposed algorithm is verified by the numerical simulation results.
topic Closed-loop system
basis pursuit de-noising
system identification
time-delay estimation
url https://ieeexplore.ieee.org/document/9015976/
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