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Iterative learning control for robotic path following with trial-varying motion profiles

Iterative learning control for robotic path following with trial-varying motion profiles
Iterative learning control for robotic path following with trial-varying motion profiles
Iterative learning control (ILC) aims to maximize the performance of systems performing repeated tracking tasks. However, in most existing applications, the motion profile is inherently specified a priori, which has restricted both its application range and scope of performance improvement. For example, the typical repeated path-following task in robotics only requires a spatial path profile rather than a temporal trajectory profile, for which most existing ILC designs are unsuitable. To handle this requirement, this article extends the ILC task description by relaxing this postulate to enable a trial-varying motion profile and formulate an ILC path-following problem with system constraints. Under this extended problem setup, a spatial ILC algorithm is proposed with efficient implementation and robust convergence analysis, which updates the input signal and motion profile at the end of each trial to reduce the tracking error. This algorithm is implemented experimentally on a gantry robot test platform to verify performance, practical feasibility, and reliability. Comparisons with other control methods are also made to clarify its advantages, such as error reduction, control effort reduction, and constraint handling.
Convergence, Gantry robot, IEEE transactions, Iterative learning control, Mechatronics, Robot kinematics, Task analysis, Trajectory, iterative learning control (ILC), path following
1083-4435
4697-4706
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chen, Yiyang, Chu, Bing and Freeman, Christopher (2022) Iterative learning control for robotic path following with trial-varying motion profiles. IEEE/ASME Transactions on Mechatronics, 27 (6), 4697-4706. (doi:10.1109/TMECH.2022.3164101).

Record type: Article

Abstract

Iterative learning control (ILC) aims to maximize the performance of systems performing repeated tracking tasks. However, in most existing applications, the motion profile is inherently specified a priori, which has restricted both its application range and scope of performance improvement. For example, the typical repeated path-following task in robotics only requires a spatial path profile rather than a temporal trajectory profile, for which most existing ILC designs are unsuitable. To handle this requirement, this article extends the ILC task description by relaxing this postulate to enable a trial-varying motion profile and formulate an ILC path-following problem with system constraints. Under this extended problem setup, a spatial ILC algorithm is proposed with efficient implementation and robust convergence analysis, which updates the input signal and motion profile at the end of each trial to reduce the tracking error. This algorithm is implemented experimentally on a gantry robot test platform to verify performance, practical feasibility, and reliability. Comparisons with other control methods are also made to clarify its advantages, such as error reduction, control effort reduction, and constraint handling.

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Accepted/In Press date: 29 March 2022
e-pub ahead of print date: 22 April 2022
Published date: 1 December 2022
Additional Information: Funding Information: This work was supported in part by Excellent Young Scholar Program of Soochow University, in part by the ZZU-Southampton Collaborative Research Project under Grant 16306/01, in part by National Natural Science Foundation of China under Grant 62103293 and Grant 61773232, in part by Natural Science Foundation of Jiangsu Province under Grant BK20210709, in part by Suzhou Municipal Science and Technology Bureau under Grant SYG202138, and in part by Royal Society International Exchanges Award under Grant IE161369 Publisher Copyright: © 1996-2012 IEEE.
Keywords: Convergence, Gantry robot, IEEE transactions, Iterative learning control, Mechatronics, Robot kinematics, Task analysis, Trajectory, iterative learning control (ILC), path following

Identifiers

Local EPrints ID: 457577
URI: http://eprints.soton.ac.uk/id/eprint/457577
ISSN: 1083-4435
PURE UUID: ffecfec9-0e7d-4b97-98b0-69619e7e5e31
ORCID for Yiyang Chen: ORCID iD orcid.org/0000-0001-9960-9040
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

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Date deposited: 13 Jun 2022 16:44
Last modified: 02 Feb 2023 02:43

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Contributors

Author: Yiyang Chen ORCID iD
Author: Bing Chu ORCID iD
Author: Christopher Freeman

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