Multivariable Decoupling Active Disturbance Rejection Control of Poultry House System Based on Multidimensional Particle Swarm Optimization (PSO) Algorithm

Abstract

Poultry farming is a vital component of the agricultural sector, requiring meticulous management to optimize bird health conditions and productivity. However, its process is more and more challenging to control due to the internal coupling effects between the climatic output variables and the input ones, particularly given the threat of significant external climate change as well as the frequent use of simple conventional controllers with poor performance due to poultry farmers' inability to implement advanced controllers. As a result, based on a complete mathematical microclimate model, this paper proposes an improved method for designing Decoupling Active Disturbance Rejection Control (DADRC) optimized by using the multidimensional Particle Swarm Optimization (PSO) algorithm for a broiler livestock building. Simulation experiments have been performed on the Multi-Inputs Multi-Outputs (MIMO) system by using a specific number of steps from the chosen approach. Compared to the Decoupling Proportional, Integral, Derivative (DPID) controller, simulation results illustrate the effectiveness of the PSO-DADRC in terms of disturbances rejection, tracking setpoints, stabilization, and performance indices. This effective implementation of decoupling active disturbance rejection control to the poultry house model demonstrates its potential for field-testing in future livestock industries with increasing demand, product quality requirements, and climate change perturbations.