An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press

To improve the performance of hydraulic press position control and eliminate the need to manually define control signals, this paper proposes a multi-input-multi-output (MIMO) Iterative Learning Control (ILC) algorithm. The MIMO ILC algorithm design is based on the inversion of the known low frequen...

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Autores principales:	Ignacio Trojaola, Iker Elorza, Eloy Irigoyen, Aron Pujana-Arrese, Gorka Sorrosal
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Iterative learning control position control MIMO electro-hydraulics Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4ba775a3b4834f389cb5b517b5e964b4
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id	oai:doaj.org-article:4ba775a3b4834f389cb5b517b5e964b4
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spelling	oai:doaj.org-article:4ba775a3b4834f389cb5b517b5e964b42021-11-09T00:02:47ZAn Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press2169-353610.1109/ACCESS.2021.3123668https://doaj.org/article/4ba775a3b4834f389cb5b517b5e964b42021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591617/https://doaj.org/toc/2169-3536To improve the performance of hydraulic press position control and eliminate the need to manually define control signals, this paper proposes a multi-input-multi-output (MIMO) Iterative Learning Control (ILC) algorithm. The MIMO ILC algorithm design is based on the inversion of the known low frequency dynamics of the hydraulic press, whereas the unknown and uncertain high frequency dynamics are discarded due to their low influence in the learning transient. Moreover, for the MIMO ILC convergence condition, a graphical method is proposed, in which the ILC learning filter eigenvalues are analyzed. This method allows studying the stability and convergence rate of the algorithm intuitively. Theoretical analysis and results prove that with the MIMO ILC algorithm the position control is automated and that high precision in the position tracking is gained. A comparison with other model inverse ILC approaches is carried out and it is shown that the proposed MIMO ILC algorithm outperforms the existing algorithms, reducing the number of iterations required to converge while guaranteeing system stability. Furthermore, experimental results in a hydraulic test rig are presented and compared to those obtained with a conventional PI controller.Ignacio TrojaolaIker ElorzaEloy IrigoyenAron Pujana-ArreseGorka SorrosalIEEEarticleIterative learning controlposition controlMIMOelectro-hydraulicsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146850-146867 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Iterative learning control position control MIMO electro-hydraulics Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Iterative learning control position control MIMO electro-hydraulics Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ignacio Trojaola Iker Elorza Eloy Irigoyen Aron Pujana-Arrese Gorka Sorrosal An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
description	To improve the performance of hydraulic press position control and eliminate the need to manually define control signals, this paper proposes a multi-input-multi-output (MIMO) Iterative Learning Control (ILC) algorithm. The MIMO ILC algorithm design is based on the inversion of the known low frequency dynamics of the hydraulic press, whereas the unknown and uncertain high frequency dynamics are discarded due to their low influence in the learning transient. Moreover, for the MIMO ILC convergence condition, a graphical method is proposed, in which the ILC learning filter eigenvalues are analyzed. This method allows studying the stability and convergence rate of the algorithm intuitively. Theoretical analysis and results prove that with the MIMO ILC algorithm the position control is automated and that high precision in the position tracking is gained. A comparison with other model inverse ILC approaches is carried out and it is shown that the proposed MIMO ILC algorithm outperforms the existing algorithms, reducing the number of iterations required to converge while guaranteeing system stability. Furthermore, experimental results in a hydraulic test rig are presented and compared to those obtained with a conventional PI controller.
format	article
author	Ignacio Trojaola Iker Elorza Eloy Irigoyen Aron Pujana-Arrese Gorka Sorrosal
author_facet	Ignacio Trojaola Iker Elorza Eloy Irigoyen Aron Pujana-Arrese Gorka Sorrosal
author_sort	Ignacio Trojaola
title	An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
title_short	An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
title_full	An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
title_fullStr	An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
title_full_unstemmed	An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press
title_sort	innovative mimo iterative learning control approach for the position control of a hydraulic press
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4ba775a3b4834f389cb5b517b5e964b4
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An Innovative MIMO Iterative Learning Control Approach for the Position Control of a Hydraulic Press

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