Iterative learning control for path following tasks with performance optimization
Iterative learning control for path following tasks with performance optimization
The classical problem setup of iterative learning control (ILC) is to enforce tracking of a reference profile specified at all time points in the fixed task duration. The removal of the time specification releases significant design freedom in how the path is followed but has not been fully exploited in the literature. This article unlocks this extra design freedom by formulating the ILC task description to handle repeated path-following tasks, e.g., welding and laser cutting, which aim at following a given ``spatial'' path defined in the output space without any temporal information. The general ILC problem is reformulated for ILC design with the inclusion of an additional performance index, and the class of piecewise linear paths is characterized for the reformulated problem setup. A two-stage design framework is proposed to solve the characterized problem and yields a comprehensive algorithm based on an ILC update and a gradient projection update. This algorithm is verified on a gantry robot experimental platform to demonstrate its practical efficacy and robustness against model uncertainty.
Iterative learning control (ILC), optimization, path following.
234-246
Chen, Yiyang
da753778-ba38-4f95-ad29-b78ff9b12b05
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
1 January 2022
Chen, Yiyang
da753778-ba38-4f95-ad29-b78ff9b12b05
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 path following tasks with performance optimization.
IEEE Transactions on Control Systems Technology, 30 (1), .
(doi:10.1109/TCST.2021.3062223).
Abstract
The classical problem setup of iterative learning control (ILC) is to enforce tracking of a reference profile specified at all time points in the fixed task duration. The removal of the time specification releases significant design freedom in how the path is followed but has not been fully exploited in the literature. This article unlocks this extra design freedom by formulating the ILC task description to handle repeated path-following tasks, e.g., welding and laser cutting, which aim at following a given ``spatial'' path defined in the output space without any temporal information. The general ILC problem is reformulated for ILC design with the inclusion of an additional performance index, and the class of piecewise linear paths is characterized for the reformulated problem setup. A two-stage design framework is proposed to solve the characterized problem and yields a comprehensive algorithm based on an ILC update and a gradient projection update. This algorithm is verified on a gantry robot experimental platform to demonstrate its practical efficacy and robustness against model uncertainty.
Text
bare_jrnl_20201109
- Accepted Manuscript
More information
Accepted/In Press date: 22 February 2021
e-pub ahead of print date: 10 March 2021
Published date: 1 January 2022
Additional Information:
Publisher Copyright:
© 1993-2012 IEEE.
Keywords:
Iterative learning control (ILC), optimization, path following.
Identifiers
Local EPrints ID: 448050
URI: http://eprints.soton.ac.uk/id/eprint/448050
ISSN: 1063-6536
PURE UUID: 7b1954f9-494f-409f-a3c3-54ca81981355
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Date deposited: 01 Apr 2021 15:40
Last modified: 17 Mar 2024 03:28
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Contributors
Author:
Yiyang Chen
Author:
Bing Chu
Author:
Christopher Freeman
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