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Iterative Learning Control with Mixed Constraints for Point-to-Point Tracking

Iterative Learning Control with Mixed Constraints for Point-to-Point Tracking
Iterative Learning Control with Mixed Constraints for Point-to-Point Tracking
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. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference.
1063-6536
604-616
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 (2012) Iterative Learning Control with Mixed Constraints for Point-to-Point Tracking. IEEE Transactions on Control Systems Technology, 21 (3), 604-616.

Record type: Article

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. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference.

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Published date: 2012
Organisations: EEE

Identifiers

Local EPrints ID: 272984
URI: http://eprints.soton.ac.uk/id/eprint/272984
ISSN: 1063-6536
PURE UUID: 8650d8be-6118-4438-96d5-83df8bda5e7b

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Date deposited: 09 Nov 2011 00:21
Last modified: 30 Sep 2020 16:32

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