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.