Broiler FCR optimization using norm optimal terminal iterative learning control
Broiler FCR optimization using norm optimal terminal iterative learning control
Broiler feed conversion rate (FCR) optimization reduces the amount of feed, water, and electricity required to produce a mature broiler, where temperature control is one of the most influential factors. Iterative learning control (ILC) provides a potential solution given the repeated nature of the production process, as it has been especially developed for systems that make repeated executions of the same finite duration task. Dynamic neural network models provide a basis for control synthesis, as no first-principle mathematical models of the broiler growth process exist. The final FCR at slaughter is one of the primary performance parameters for broiler production, and it is minimized using a modified terminal ILC law in this article. Simulation evaluation of the new designs is undertaken using a heuristic broiler growth model based on the knowledge of a broiler application expert and experimentally on a state-of-the-art broiler house that produces approximately 40000 broilers per batch.
580-592
Johansen, Simon V.
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Jensen, Martin R.
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Chu, Bing
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Bendtsen, Jan D
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Mogensen, Jesper
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Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
9 February 2021
Johansen, Simon V.
3a497b7e-72b2-4acd-beee-f747e4d7a7f8
Jensen, Martin R.
d1afa95e-3f8d-488b-8a16-f706f16ab005
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Bendtsen, Jan D
0d2db5d6-2fdf-4fc1-a9ea-1b93cc0f5105
Mogensen, Jesper
e4efe587-f616-4767-a9ed-24cf2894772b
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Johansen, Simon V., Jensen, Martin R., Chu, Bing, Bendtsen, Jan D, Mogensen, Jesper and Rogers, Eric
(2021)
Broiler FCR optimization using norm optimal terminal iterative learning control.
IEEE Transactions on Control Systems Technology, 29 (2), .
(doi:10.1109/TCST.2019.2954300).
Abstract
Broiler feed conversion rate (FCR) optimization reduces the amount of feed, water, and electricity required to produce a mature broiler, where temperature control is one of the most influential factors. Iterative learning control (ILC) provides a potential solution given the repeated nature of the production process, as it has been especially developed for systems that make repeated executions of the same finite duration task. Dynamic neural network models provide a basis for control synthesis, as no first-principle mathematical models of the broiler growth process exist. The final FCR at slaughter is one of the primary performance parameters for broiler production, and it is minimized using a modified terminal ILC law in this article. Simulation evaluation of the new designs is undertaken using a heuristic broiler growth model based on the knowledge of a broiler application expert and experimentally on a state-of-the-art broiler house that produces approximately 40000 broilers per batch.
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e-pub ahead of print date: 16 December 2019
Published date: 9 February 2021
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Local EPrints ID: 469275
URI: http://eprints.soton.ac.uk/id/eprint/469275
ISSN: 1063-6536
PURE UUID: dfc18d98-8eca-4f96-86ae-4650cc5216a7
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Date deposited: 12 Sep 2022 16:44
Last modified: 17 Mar 2024 03:28
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Author:
Simon V. Johansen
Author:
Martin R. Jensen
Author:
Bing Chu
Author:
Jan D Bendtsen
Author:
Jesper Mogensen
Author:
Eric Rogers
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