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Broiler growth optimization using norm optimal terminal iterative learning control

Broiler growth optimization using norm optimal terminal iterative learning control
Broiler growth optimization using norm optimal terminal iterative learning control

Broiler (chicken for meat production) growth maximization 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 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 are used given the absence of mathematical models of the growth process. Traditional ILC is modified to maximize the terminal broiler weight and better cope with the uncertain nature of the data driven model. To evaluate the proposed algorithm in simulation, a heuristic broiler growth model based on the knowledge of a broiler application expert is formalized. This paper gives the first results on the application of optimization based iterative learning control.

Biosystems, Iterative learning control, Neural networks
1258-1264
IEEE
Johansen, Simon V.
3a497b7e-72b2-4acd-beee-f747e4d7a7f8
Jensen, Martin R.
d1afa95e-3f8d-488b-8a16-f706f16ab005
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Bcndtsen, Jan D.
89403e0a-01a2-438c-8d43-492daead5ed6
Mogensen, Jesper
8038b695-125c-4985-9ad0-b0db6cdec5c4
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Johansen, Simon V.
3a497b7e-72b2-4acd-beee-f747e4d7a7f8
Jensen, Martin R.
d1afa95e-3f8d-488b-8a16-f706f16ab005
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Bcndtsen, Jan D.
89403e0a-01a2-438c-8d43-492daead5ed6
Mogensen, Jesper
8038b695-125c-4985-9ad0-b0db6cdec5c4
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Johansen, Simon V., Jensen, Martin R., Chu, Bing, Bcndtsen, Jan D., Mogensen, Jesper and Rogers, Eric (2018) Broiler growth optimization using norm optimal terminal iterative learning control. In 2018 IEEE Conference on Control Technology and Applications, CCTA 2018. IEEE. pp. 1258-1264 . (doi:10.1109/CCTA.2018.8511464).

Record type: Conference or Workshop Item (Paper)

Abstract

Broiler (chicken for meat production) growth maximization 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 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 are used given the absence of mathematical models of the growth process. Traditional ILC is modified to maximize the terminal broiler weight and better cope with the uncertain nature of the data driven model. To evaluate the proposed algorithm in simulation, a heuristic broiler growth model based on the knowledge of a broiler application expert is formalized. This paper gives the first results on the application of optimization based iterative learning control.

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More information

Published date: 29 October 2018
Venue - Dates: 2nd IEEE Conference on Control Technology and Applications, CCTA 2018, Copenhagen, Denmark, 2018-08-21 - 2018-08-24
Keywords: Biosystems, Iterative learning control, Neural networks

Identifiers

Local EPrints ID: 426644
URI: http://eprints.soton.ac.uk/id/eprint/426644
PURE UUID: e89027b0-213b-4b44-aae6-8e0a64769250
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 07 Dec 2018 17:30
Last modified: 20 Jul 2019 01:24

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Contributors

Author: Simon V. Johansen
Author: Martin R. Jensen
Author: Bing Chu ORCID iD
Author: Jan D. Bcndtsen
Author: Jesper Mogensen
Author: Eric Rogers ORCID iD

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