Broiler FCR Optimization Using Norm Optimal Terminal Iterative Learning Control

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.