The University of Southampton
University of Southampton Institutional Repository

On Experimentally Validated Iterative Learning Control in Human Motor Systems

On Experimentally Validated Iterative Learning Control in Human Motor Systems
On Experimentally Validated Iterative Learning Control in Human Motor Systems
A framework is developed to construct computational models of the human motor system (HMS) using various iterative learning control (ILC) update structures. Optimal models of movement are introduced using a general cost function (involving both tracking objective and an additional constraint term), and its parameters are fitted to observed limiting solutions corresponding to learned human motion obtained from experiments. Three general ILC update structures are considered which each generate the required limiting solution using different forms of experimental data. It is shown how the parameters in each which govern convergence may also be fitted to experimental learning data, with the different ILC structures permitting varying degrees of freedom in capturing the observed learning transients. Experimental results in which a participant uses a planar robot to perform reaching tasks confirm the ability of the proposed ILC structures to accurately model the learning ability of the human motor system.
4262-4267
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Zhou, S.-H.
f1482119-bc34-41bd-9b08-737ab2109d3e
Tan, Y,
92b100aa-04b0-46c5-a7ce-b9781525af1f
Oetomo, D.
a28f79d5-fc16-4aea-bd0e-08c73a94c8b9
Burdet, E.
c53b4a27-b050-4862-9f6d-81971f6fa2be
Mareels, I.
bf94891c-18ec-4866-95b9-4fb1b411732a
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Zhou, S.-H.
f1482119-bc34-41bd-9b08-737ab2109d3e
Tan, Y,
92b100aa-04b0-46c5-a7ce-b9781525af1f
Oetomo, D.
a28f79d5-fc16-4aea-bd0e-08c73a94c8b9
Burdet, E.
c53b4a27-b050-4862-9f6d-81971f6fa2be
Mareels, I.
bf94891c-18ec-4866-95b9-4fb1b411732a

Freeman, C.T., Zhou, S.-H., Tan, Y,, Oetomo, D., Burdet, E. and Mareels, I. (2014) On Experimentally Validated Iterative Learning Control in Human Motor Systems. American Control Conference. 04 - 06 Jun 2014. pp. 4262-4267 .

Record type: Conference or Workshop Item (Other)

Abstract

A framework is developed to construct computational models of the human motor system (HMS) using various iterative learning control (ILC) update structures. Optimal models of movement are introduced using a general cost function (involving both tracking objective and an additional constraint term), and its parameters are fitted to observed limiting solutions corresponding to learned human motion obtained from experiments. Three general ILC update structures are considered which each generate the required limiting solution using different forms of experimental data. It is shown how the parameters in each which govern convergence may also be fitted to experimental learning data, with the different ILC structures permitting varying degrees of freedom in capturing the observed learning transients. Experimental results in which a participant uses a planar robot to perform reaching tasks confirm the ability of the proposed ILC structures to accurately model the learning ability of the human motor system.

Full text not available from this repository.

More information

Published date: 18 January 2014
Venue - Dates: American Control Conference, 2014-06-04 - 2014-06-06
Organisations: EEE

Identifiers

Local EPrints ID: 361364
URI: http://eprints.soton.ac.uk/id/eprint/361364
PURE UUID: c9faa24e-a8ab-43df-b38d-d4241290da20

Catalogue record

Date deposited: 18 Jan 2014 16:10
Last modified: 16 Jul 2019 21:13

Export record

Contributors

Author: C.T. Freeman
Author: S.-H. Zhou
Author: Y, Tan
Author: D. Oetomo
Author: E. Burdet
Author: I. Mareels

University divisions

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×