Multi-loop active disturbance rejection control and PID control strategy for poultry house based on GA, PSO and GWO algorithms.

Maintaining the health and welfare of broilers, besides obtaining and optimizing good performance, are the main objectives of poultry production. In response, climate control remains the most guaranteed strategy for managing livestock successfully. Separate controlling temperature and humidity on the one hand; and contaminant gases on the other was a focus of several investigations. Thus, the particularity of this work which involves the study, analysis, and control of broiler livestock building while taking into account, at the same time, all the system's constituent variables (i.e., temperature, humidity, NH3 and CO2 concentration, air velocity, and differential pressure). In this paper, an Active Disturbance Rejection Control (ADRC) and Proportional Integral Derivative (PID) controllers were designed and combined with a multi-loop approach for a multi-inputs multi-outputs (MIMO) system. Then, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) were used to obtain the optimal controllers' parameters employing the reward function, the Integrated Time Absolute Error (ITAE), according to the poultry system requirements. Simulation experiments were carried out using the Matlab Simulink toolbox to verify the effectiveness of all the proposed control methods with the two optimization algorithms regarding stabilization and tracking setpoints. Despite the introduction of several disturbances in the plant model, the PSO-ADRC controller still exhibits notable benefits in terms of rise time, overshoot, settling time, and good disturbance rejection, proving the robustness of the suggested control method.

Modeling and identification of the hygro-thermal parameters of mechanically-ventilated broiler house using prediction error: Assessment in cold conditions.

Indoor air temperature and humidity moisture are of the foremost significance in climate control of broilers houses, and their impacts on poultry health and production depend on accurate control. The main objective of this work is to identify and assess a novel state-space model, to rapidly predict the hygro-thermal behavior of the livestock building. To achieve this analysis, various experimental measurements (e.g., ventilation rate, thermal heating, and air temperature and humidity) of two commercial poultry houses placed in the Mediterranean zone were monitored over cold conditions production cycle. The developed model was estimated and validated against a dataset of 25 days acquired under three different operation ventilation modes (min-ven, power and tunnel modes). Through simulation, the results showed that the predicted model and measured data were achieved a satisfactory accuracy with an averaged coefficients of determination R2 were 0.93 and 0.95, respectively, for the indoor air temperature and humidity models, and a root mean squared error (RMSE) of 0.3213 °C and 0.957 %. Additionally, the predictive model shows satisfying performances for the long horizon prediction with a final prediction error (FPE) equal to 0.084, which will prevent the intensely time-consuming process of getting precise physical parameters in regards the poultry house system.

Towards modelling, and analysis of differential pressure and air velocity in a mechanical ventilation poultry house: Application for hot climates.

Due to the broiler house's needs for a healthy environment, efficient control system, and appropriate air, several studies were interested in microclimate and air quality characteristics. However, limited studies are conducted to investigate pressure and air velocity within poultry buildings, which are also significant parameters that impact the breeding environment and productivity. As a reason, the objective of this work was to develop a mathematical model exploring the differential pressure and air velocity inside the house. The peculiarity of this research is the use of thermal balance and air properties to propose a model related to birds' weight which can be translated to birds' age and thermal conditions. The proposed approach acquired experimental measurements (e.g., indoor air temperature and humidity, air velocity, and differential pressure) and performed simulations in a mechanically ventilated Mediterranean broiler house over a summer production cycle. The findings revealed that the observed and modelled differential pressure ranged from a negative to a positive pressure (-5 to 39 Pa), with broilers subjected to air velocity varying from 0.09 to 1.641 m s-1 depending on three distinct modes of regulation: nature, power, and tunnel mode. These results confirmed the model's predictive capacity with a relative error of 1.03% of differential pressure and 0.68% of air velocity and a normalised mean square error (NMSE) of -1.06 Pa and 0.19 m s-1, respectively. Consequently, the methodology applied in this paper may be extended to various species of breeding structures in other seasons, allowing simulation tools and system control improvement.

Corrigendum to "Experimental implementation of a new multi input multi output fuzzy-PID controller in a poultry house system" [Heliyon 6 (8) (2020) e04645].

[This corrects the article DOI: 10.1016/j.heliyon.2020.e04645.].

Experimental implementation of a new multi input multi output fuzzy-PID controller in a poultry house system.

Broiler house systems are operated for the primary purpose of providing the appropriate conditions suitable to have a significant efficiency of animal production. The major environmental conditions in the poultry building are controlling the hygro-thermal parameters (temperature and relative humidity) and contaminant gases (NH3, CO2). In this paper, a poultry house prototype is monitored and controlled using the Supervisory Control and Data Acquisition (SCADA) tool like LabVIEW. A full prototype is designed and an efficient hybrid control strategy is implemented to control in real-time the poultry house climate. In the suggested approach, a Multi-Input Multi-Output (MIMO) fuzzy logic controller (FLC) is combined with a proportional, integral, derivative (PID) controller tuned by fuzzy rules. The proposed method, fuzzy logic, and On/Off controllers were tested by experimental measures and studies in a prototype model over 30 days during hot climates. The comparison results showed that the root mean square error of temperature and relative humidity response with the MFLPID controller (0.8 °C, 1.34%) were lower than that of FLC (1.16 °C, 1.86%) and On/Off controller (2.09 °C, 3.08%). The mean value of CO2 concentration with MFLPID (2461 ppm) was lower than that of FLC (3294 ppm) and On/Off controller (3624 ppm). However, the mean value of NH3 concentration was limited in small value (<5 ppm) for all controllers. The performance of the daily weight gained by the chickens for the MFLPID system was found to be 97%, which is higher than that of FLC (88%) and On/Off (80%). The energy consumption of the actuators can be saved at 43% and 14% with MFLPID compared to the On/Off and fuzzy controllers. These results indicate that the proposed control strategy is more efficient in the application of the poultry farming sector.