Iterative learning control of minimum energy path following tasks for second-order MIMO systems: an indirect reference update framework
Iterative learning control of minimum energy path following tasks for second-order MIMO systems: an indirect reference update framework
In a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This paper develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches.
Constraint handling, iterative learning control, optimization, path planning
3403-3416
Chen, Yiyang
bbcd67b6-eb72-4897-861e-a2bb330ca226
Wang, Yiming
b48e7f23-fe36-4bf9-b711-fece9def10ce
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
July 2025
Chen, Yiyang
bbcd67b6-eb72-4897-861e-a2bb330ca226
Wang, Yiming
b48e7f23-fe36-4bf9-b711-fece9def10ce
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Yiyang, Wang, Yiming and Freeman, Christopher T.
(2025)
Iterative learning control of minimum energy path following tasks for second-order MIMO systems: an indirect reference update framework.
IEEE Transactions on Cybernetics, 55 (7), .
(doi:10.1109/TCYB.2025.3556703).
Abstract
In a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This paper develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches.
Text
Yiyang_s_Trans_Paper
- Accepted Manuscript
More information
e-pub ahead of print date: 15 April 2025
Published date: July 2025
Keywords:
Constraint handling, iterative learning control, optimization, path planning
Identifiers
Local EPrints ID: 500706
URI: http://eprints.soton.ac.uk/id/eprint/500706
ISSN: 2168-2267
PURE UUID: a1b5e0a5-4762-4c4f-96d0-d80fb1a6cdd1
Catalogue record
Date deposited: 12 May 2025 16:31
Last modified: 28 Aug 2025 01:42
Export record
Altmetrics
Contributors
Author:
Yiyang Chen
Author:
Yiming Wang
Author:
Christopher T. Freeman
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics