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Iterative learning control for minimum time path following

Iterative learning control for minimum time path following
Iterative learning control for minimum time path following
The technique of iterative learning control (ILC) is frequently applied to improve the tracking performance of those systems operating repetitively. This paper extends the ILC task description to handle path following problems by relaxing assumptions on fixed motion profiles and trial length. The removal of these design constraints yields significant control design flexibilities to choose an admissible solution of the problem so that an extra performance index is optimized. A two stage design framework is considered to find the minimum path following time, and guarantee a feasible solution of the path following problem under system constraints using ILC. The solutions of the two stages are provided by a norm optimal ILC update and the bisection method with implementation guidelines, which are combined to form a comprehensive implementation algorithm. The effectiveness of the proposed algorithm is evaluated using a case study on a gantry robot model.
Chen, Yiyang
da753778-ba38-4f95-ad29-b78ff9b12b05
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
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 (2019) Iterative learning control for minimum time path following. In 13th IFAC Workshop on Adaptive and Learning Control Systems. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

The technique of iterative learning control (ILC) is frequently applied to improve the tracking performance of those systems operating repetitively. This paper extends the ILC task description to handle path following problems by relaxing assumptions on fixed motion profiles and trial length. The removal of these design constraints yields significant control design flexibilities to choose an admissible solution of the problem so that an extra performance index is optimized. A two stage design framework is considered to find the minimum path following time, and guarantee a feasible solution of the path following problem under system constraints using ILC. The solutions of the two stages are provided by a norm optimal ILC update and the bisection method with implementation guidelines, which are combined to form a comprehensive implementation algorithm. The effectiveness of the proposed algorithm is evaluated using a case study on a gantry robot model.

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More information

Published date: 4 December 2019
Venue - Dates: 13th IFAC Workshop on Adaptive and Learning Control Systems, United Kingdom, 2019-12-04 - 2019-12-06

Identifiers

Local EPrints ID: 436763
URI: http://eprints.soton.ac.uk/id/eprint/436763
PURE UUID: 05a725eb-5214-4125-9fd0-e7562ad200c5
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 03 Jan 2020 17:30
Last modified: 27 Jan 2020 13:45

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