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 most influential factors. Iterative learning (ILC) provides potential solution given repeated nature production process, as it has been especially developed for systems that make executions same finite duration task. Dynamic neural network models provide basis synthesis, no first-principle mathematical broiler growth process exist. The final FCR at slaughter primary performance parameters production, minimized using modified terminal ILC law in this article. Simulation evaluation new designs undertaken heuristic model based on knowledge application expert experimentally state-of-the-art house produces approximately 40000 broilers per batch.