Norm optimal iterative learning control: A data-driven approach
Norm optimal iterative learning control: A data-driven approach
Iterative learning control (ILC) is a control design method that can improve the tracking performance for systems working in a repetitive manner by learning from the previous iterations. Norm optimal ILC is a well known ILC design with appealing convergence properties, e.g. monotonic error norm convergence. However, it requires an explicit system model in the design, which can be difficult or expensive to obtain in practice. To address this problem, this paper proposes a data-driven norm optimal ILC design exploiting recent development in data-driven control. A receding horizon implementation of the design is further developed to relax the requirement on data. Convergence properties of the design are analysed rigorously and simulation examples are presented to demonstrate the effectiveness of the method.
control design, convergence analysis, data-driven control, Iterative learning control, simulation
482-487
Jiang, Zheng
d42bc017-e3fd-46bf-9db5-e05ea72dd78c
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
2022
Jiang, Zheng
d42bc017-e3fd-46bf-9db5-e05ea72dd78c
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Jiang, Zheng and Chu, Bing
(2022)
Norm optimal iterative learning control: A data-driven approach.
IFAC PAPERSONLINE, 55 (12), .
(doi:10.1016/j.ifacol.2022.07.358).
Abstract
Iterative learning control (ILC) is a control design method that can improve the tracking performance for systems working in a repetitive manner by learning from the previous iterations. Norm optimal ILC is a well known ILC design with appealing convergence properties, e.g. monotonic error norm convergence. However, it requires an explicit system model in the design, which can be difficult or expensive to obtain in practice. To address this problem, this paper proposes a data-driven norm optimal ILC design exploiting recent development in data-driven control. A receding horizon implementation of the design is further developed to relax the requirement on data. Convergence properties of the design are analysed rigorously and simulation examples are presented to demonstrate the effectiveness of the method.
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e-pub ahead of print date: 4 August 2022
Published date: 2022
Additional Information:
Publisher Copyright:
© 2022 Elsevier B.V.. All rights reserved.
Venue - Dates:
14th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2022, , Casablanca, Morocco, 2022-06-29 - 2022-07-01
Keywords:
control design, convergence analysis, data-driven control, Iterative learning control, simulation
Identifiers
Local EPrints ID: 471609
URI: http://eprints.soton.ac.uk/id/eprint/471609
ISSN: 2405-8963
PURE UUID: 834aa526-e858-48b9-b8d1-dba85049f05c
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Date deposited: 14 Nov 2022 18:11
Last modified: 17 Mar 2024 03:28
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Author:
Zheng Jiang
Author:
Bing Chu
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