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Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking
Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking
Norm Optimal Iterative Learning Control (NOILC) has recently been applied to Iterative Learning Control (ILC)
problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix ‘point to point’ movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC’s well-defined convergence properties. Design guidelines and supporting
experimental results are included.
1476-8186
243-253
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f

Owens, David H., Freeman, Christopher and Chu, Bing (2015) Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking. International Journal of Automation and Computing, 12 (3), 243-253.

Record type: Article

Abstract

Norm Optimal Iterative Learning Control (NOILC) has recently been applied to Iterative Learning Control (ILC)
problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix ‘point to point’ movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC’s well-defined convergence properties. Design guidelines and supporting
experimental results are included.

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Published date: 1 March 2015
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 363593
URI: http://eprints.soton.ac.uk/id/eprint/363593
ISSN: 1476-8186
PURE UUID: 52ad0106-5739-492d-8549-f16f9647cd39
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

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Date deposited: 26 Mar 2014 21:09
Last modified: 22 Oct 2019 00:37

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