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Experimentally verified point-to-point iterative learning control for highly coupled systems

Experimentally verified point-to-point iterative learning control for highly coupled systems
Experimentally verified point-to-point iterative learning control for highly coupled systems
Iterative learning control (ILC) is a well established approach for precision tracking control of systems which
perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework
accompanied by a wide array of application studies, comparatively little attention has been paid to the
case of multiple input, multiple output (MIMO) systems. Here the presence of interacting dynamics often
correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and
establishes links between interaction dynamics and reduced robustness to modelling uncertainty, and slower
convergence. It then shows how these and other limitations can be addressed by relaxing the tracking
requirement to include only a subset of time points along the time duration. This is the first analysis to
show how so-called ‘point-to-point’ ILC can address performance limitations associated with highly coupled
systems. Theoretical observations are tested using a novel MIMO experimental test facility which permits
both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results
compare experimental observations with theoretical predictions over a wide range of interaction levels and
with varying levels of injected noise.
0890-6327
302-324
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Dinh Van, T.
8656dc1c-405b-4001-a405-42dd074187e5
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Dinh Van, T.
8656dc1c-405b-4001-a405-42dd074187e5

Freeman, C.T. and Dinh Van, T. (2014) Experimentally verified point-to-point iterative learning control for highly coupled systems. International Journal of Adaptive Control and Signal Processing, 89 (3), 302-324. (doi:10.1002/acs.2472).

Record type: Article

Abstract

Iterative learning control (ILC) is a well established approach for precision tracking control of systems which
perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework
accompanied by a wide array of application studies, comparatively little attention has been paid to the
case of multiple input, multiple output (MIMO) systems. Here the presence of interacting dynamics often
correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and
establishes links between interaction dynamics and reduced robustness to modelling uncertainty, and slower
convergence. It then shows how these and other limitations can be addressed by relaxing the tracking
requirement to include only a subset of time points along the time duration. This is the first analysis to
show how so-called ‘point-to-point’ ILC can address performance limitations associated with highly coupled
systems. Theoretical observations are tested using a novel MIMO experimental test facility which permits
both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results
compare experimental observations with theoretical predictions over a wide range of interaction levels and
with varying levels of injected noise.

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Published date: 2014
Organisations: EEE

Identifiers

Local EPrints ID: 344296
URI: http://eprints.soton.ac.uk/id/eprint/344296
ISSN: 0890-6327
PURE UUID: c7d31361-15cb-4de6-b058-f3b930fe18ba

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Date deposited: 16 Oct 2012 23:23
Last modified: 02 Dec 2019 20:55

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