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Point-to-Point Iterative Learning Control with Mixed Constraints

Point-to-Point Iterative Learning Control with Mixed Constraints
Point-to-Point Iterative Learning Control with Mixed Constraints
Iterative learning control is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. In this paper iterative learning schemes are developed to address the case in which the output is only critical at certain time instants. This freedom makes it possible to incorporate both hard and soft constraints into the control scheme. Experimental results confirm practically and performance.
3657-3662
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Tan, Ying
23bafadb-0655-48fe-9937-c59f01cb58ab
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Tan, Ying
23bafadb-0655-48fe-9937-c59f01cb58ab

Freeman, Christopher and Tan, Ying (2011) Point-to-Point Iterative Learning Control with Mixed Constraints. 2011 American Control Conference, San Francisco, California, United States. 29 Jun - 01 Jul 2011. pp. 3657-3662 .

Record type: Conference or Workshop Item (Paper)

Abstract

Iterative learning control is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. In this paper iterative learning schemes are developed to address the case in which the output is only critical at certain time instants. This freedom makes it possible to incorporate both hard and soft constraints into the control scheme. Experimental results confirm practically and performance.

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Published date: 29 June 2011
Additional Information: Event Dates: June 29 - July 1, 2011
Venue - Dates: 2011 American Control Conference, San Francisco, California, United States, 2011-06-29 - 2011-07-01
Organisations: EEE

Identifiers

Local EPrints ID: 271608
URI: http://eprints.soton.ac.uk/id/eprint/271608
PURE UUID: e973acce-41f8-4f03-a3cd-bb2fb9da6eed

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Date deposited: 02 Oct 2010 12:38
Last modified: 14 Mar 2024 09:35

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Contributors

Author: Christopher Freeman
Author: Ying Tan

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