Neural Predictive Control of Broiler Chicken Growth

Abstract

Active control of the growth of broiler chickens has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent broiler chicken growth using a recently developed nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler weights accurately followed the desired growth curves ranging from $-12%$ to +12% of the standard curve. The overall mean relative error between the desired and achieved broiler weight was 1.8% for the period from day 12 to day 51.