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Spatial path tracking using iterative learning control

Spatial path tracking using iterative learning control
Spatial path tracking using iterative learning control
This paper proposes a novel control methodology to enable accurate tracking of a path profile defined in output space. No temporal requirement is specified on this movement a priori, and the proposed framework enforces path tracking while minimizing an additional objective function. The problem is solved by formulating the problem as a constrained optimization involving simultaneous spatial tracking constraints and temporal via-point constraints. Practical implementation is via a two stage iterative learning control algorithm based on norm optimal and gradient updates which embeds robustness to plant uncertainty. The algorithm is verified using a gantry robot experimental platform, whose results reveal practical efficacy.
0743-1546
7189-7194
IEEE
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chen, Yiyang
2633396c-fcb8-4b50-8104-3d0da5d734cc
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chen, Yiyang, Chu, Bing and Freeman, Christopher T. (2016) Spatial path tracking using iterative learning control. In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE. pp. 7189-7194 . (doi:10.1109/CDC.2016.7799378).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper proposes a novel control methodology to enable accurate tracking of a path profile defined in output space. No temporal requirement is specified on this movement a priori, and the proposed framework enforces path tracking while minimizing an additional objective function. The problem is solved by formulating the problem as a constrained optimization involving simultaneous spatial tracking constraints and temporal via-point constraints. Practical implementation is via a two stage iterative learning control algorithm based on norm optimal and gradient updates which embeds robustness to plant uncertainty. The algorithm is verified using a gantry robot experimental platform, whose results reveal practical efficacy.

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e-pub ahead of print date: 29 December 2016
Published date: December 2016
Venue - Dates: IEEE 55th Conference on Decision and Control (CDC), Las Vegas, United States, 2016-12-12 - 2016-12-14
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 399838
URI: http://eprints.soton.ac.uk/id/eprint/399838
ISSN: 0743-1546
PURE UUID: f28867d9-81e7-4d1e-8291-d854dd2b3146
ORCID for Yiyang Chen: ORCID iD orcid.org/0000-0001-9960-9040
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 31 Aug 2016 13:55
Last modified: 10 Dec 2019 01:37

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Contributors

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

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