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# Iterative learning control for path following tasks with performance optimization

Chen, Yiyang, Chu, Bing and Freeman, Christopher (2021) Iterative learning control for path following tasks with performance optimization. IEEE Transactions on Control Systems Technology.

Record type: Article

## 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

Accepted/In Press date: 22 February 2021
e-pub ahead of print date: 10 March 2021

## Identifiers

Local EPrints ID: 448050
URI: http://eprints.soton.ac.uk/id/eprint/448050
ISSN: 1063-6536
PURE UUID: 7b1954f9-494f-409f-a3c3-54ca81981355
ORCID for Bing Chu: orcid.org/0000-0002-2711-8717

## Catalogue record

Date deposited: 01 Apr 2021 15:40

## Contributors

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