A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the pre...

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Autores principales:	Michele Pierallini, Franco Angelini, Riccardo Mengacci, Alessandro Palleschi, Antonio Bicchi, Manolo Garabini
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Iterative learning control nonlinear control systems robustness robots Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e2ce5f216307443abad9784bfb10d09e
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spelling	oai:doaj.org-article:e2ce5f216307443abad9784bfb10d09e2021-11-18T00:05:31ZA Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances2169-353610.1109/ACCESS.2021.3124014https://doaj.org/article/e2ce5f216307443abad9784bfb10d09e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592831/https://doaj.org/toc/2169-3536In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator.Michele PieralliniFranco AngeliniRiccardo MengacciAlessandro PalleschiAntonio BicchiManolo GarabiniIEEEarticleIterative learning controlnonlinear control systemsrobustnessrobotsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147471-147480 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Iterative learning control nonlinear control systems robustness robots Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Iterative learning control nonlinear control systems robustness robots Electrical engineering. Electronics. Nuclear engineering TK1-9971 Michele Pierallini Franco Angelini Riccardo Mengacci Alessandro Palleschi Antonio Bicchi Manolo Garabini A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
description	In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator.
format	article
author	Michele Pierallini Franco Angelini Riccardo Mengacci Alessandro Palleschi Antonio Bicchi Manolo Garabini
author_facet	Michele Pierallini Franco Angelini Riccardo Mengacci Alessandro Palleschi Antonio Bicchi Manolo Garabini
author_sort	Michele Pierallini
title	A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
title_short	A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
title_full	A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
title_fullStr	A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
title_full_unstemmed	A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances
title_sort	robust iterative learning control for continuous-time nonlinear systems with disturbances
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e2ce5f216307443abad9784bfb10d09e
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A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

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