Data-driven iterative learning control for continuous-time systems
Data-driven iterative learning control for continuous-time systems
We develop a data-driven iterative learning con- trol design framework for continuous-time systems that does not require explicit or implicit identification of a system model. Using Chebyshev polynomial orthogonal bases, we show that all system trajectories can be characterised from sufficiently rich input/output data. Using such crucial result we develop a data-driven version of the model-based norm-optimal iterative learning control algorithm, and provide a computationally efficient implementation thereof. We rigorously analyse the convergence properties of the resulting design and also present a numerical example to illustrate its effectiveness.
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
12 July 2023
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Chu, Bing and Rapisarda, Paolo
(2023)
Data-driven iterative learning control for continuous-time systems.
In Proceedings of the 62nd IEEE Conference on Decision and Control.
IEEE.
8 pp
.
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Conference or Workshop Item
(Paper)
Abstract
We develop a data-driven iterative learning con- trol design framework for continuous-time systems that does not require explicit or implicit identification of a system model. Using Chebyshev polynomial orthogonal bases, we show that all system trajectories can be characterised from sufficiently rich input/output data. Using such crucial result we develop a data-driven version of the model-based norm-optimal iterative learning control algorithm, and provide a computationally efficient implementation thereof. We rigorously analyse the convergence properties of the resulting design and also present a numerical example to illustrate its effectiveness.
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Published date: 12 July 2023
Venue - Dates:
62nd IEEE Conference on Decision and Control<br/>, , Singapore, 2023-12-13
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Local EPrints ID: 485679
URI: http://eprints.soton.ac.uk/id/eprint/485679
PURE UUID: 2389f3a2-e310-4756-93b9-224aa1835107
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Date deposited: 14 Dec 2023 17:30
Last modified: 18 Mar 2024 03:21
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Contributors
Author:
Bing Chu
Author:
Paolo Rapisarda
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