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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9592831/ |
| _version_ | 1819036416634519552 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T08:05:11Z |
| format | Article |
| id | doaj.art-e2ce5f216307443abad9784bfb10d09e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T08:05:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e2ce5f216307443abad9784bfb10d09e2022-12-21T19:10:50ZengIEEEIEEE Access2169-35362021-01-01914747114748010.1109/ACCESS.2021.31240149592831A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With DisturbancesMichele Pierallini0https://orcid.org/0000-0003-0547-2747Franco Angelini1https://orcid.org/0000-0003-2559-9569Riccardo Mengacci2https://orcid.org/0000-0002-2194-8437Alessandro Palleschi3https://orcid.org/0000-0001-5739-1741Antonio Bicchi4https://orcid.org/0000-0001-8635-5571Manolo Garabini5https://orcid.org/0000-0002-5873-3173Centro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio,” Università di Pisa, Pisa, ItalyIn 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.https://ieeexplore.ieee.org/document/9592831/Iterative learning controlnonlinear control systemsrobustnessrobots |
| spellingShingle | Michele Pierallini Franco Angelini Riccardo Mengacci Alessandro Palleschi Antonio Bicchi Manolo Garabini A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances IEEE Access Iterative learning control nonlinear control systems robustness robots |
| title | 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_short | 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 |
| topic | Iterative learning control nonlinear control systems robustness robots |
| url | https://ieeexplore.ieee.org/document/9592831/ |
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