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Iterative learning control with predictive trial information: convergence, robustness and experimental verifications

Iterative learning control with predictive trial information: convergence, robustness and experimental verifications
Iterative learning control with predictive trial information: convergence, robustness and experimental verifications
Iterative learning control (ILC) is a control design method for high-performance trajectory tracking. Most existing results achieve this by learning from information collected over the past executions of the task (named trials). This brief proposes a novel ILC design framework that updates the control input by learning not only from the past trials but also from the predicted future trials using knowledge of the plant model. It is shown that by including information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance can be achieved. Analysis of the algorithm's properties reveals potentially substantial benefit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Both numerical simulations and experimental results using a nonminimum phase test facility are provided to demonstrate the effectiveness of the proposed method.
convergence, experimental verification, iterative learning control (ILC), predictive control, robustness
1063-6536
1-8
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Owens, D.H.
3452e9bb-d3bd-4995-b4bb-424bbd288b09
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Chu, B., Owens, D.H. and Freeman, C.T. (2015) Iterative learning control with predictive trial information: convergence, robustness and experimental verifications. IEEE Transactions on Control Systems Technology, 1-8. (doi:10.1109/TCST.2015.2476779).

Record type: Article

Abstract

Iterative learning control (ILC) is a control design method for high-performance trajectory tracking. Most existing results achieve this by learning from information collected over the past executions of the task (named trials). This brief proposes a novel ILC design framework that updates the control input by learning not only from the past trials but also from the predicted future trials using knowledge of the plant model. It is shown that by including information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance can be achieved. Analysis of the algorithm's properties reveals potentially substantial benefit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Both numerical simulations and experimental results using a nonminimum phase test facility are provided to demonstrate the effectiveness of the proposed method.

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Accepted/In Press date: 22 August 2015
Published date: 28 September 2015
Keywords: convergence, experimental verification, iterative learning control (ILC), predictive control, robustness
Organisations: EEE

Identifiers

Local EPrints ID: 380917
URI: http://eprints.soton.ac.uk/id/eprint/380917
ISSN: 1063-6536
PURE UUID: ec96ec6f-6d8e-48a8-8ce8-7f8e0f804975
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

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Date deposited: 25 Aug 2015 14:54
Last modified: 17 Dec 2019 01:38

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Contributors

Author: B. Chu ORCID iD
Author: D.H. Owens
Author: C.T. Freeman

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