Poultry house climate control using a Fuzzy Logic Controller

Main Article Content

Nutsuporn Chinsuwan
Krisanarach Nitisiri
Niyom Pinitkarn
Thana Radpukdee
Chanoknun Sookkumnerd

Abstract

The aim of this paper is developing a Fuzzy Logic Controller (FLC) to control climate with evaporative cooling system in model of poultyr house. In recent year, many studys show that FLC has good performance and simple to apply with a complex system and poultry house climate control is nonlinear system which is complex and difficult to control. Thus we apply FLC to control this system. By internal temperature and relative humidity, important index indicating thermal comfort, is feedback signal.


In the design, 2 FLCs used for controlling fans and pump. Both FLCs use the structure of 4 inputs and 1 output. The error and the error derivative of temperature and relative humidity are the input of the system. These are translated to linguistic valuables by the membership function and through fuzzy processes according to If-Then rule which based on phase plane analysis to arrive at single value, and then defuzzification to get value of the change of ventilation rate and moisture production rate.


The maximum and average error is collected to propose FLC comparing with a conventional control system. The result shows that FLC can control temperature in ± 1.5 °C range and relative humidity control in ± 20% range. The FLC has better performance than a conventional system. For the future work , to improve relative humidity response the finer membership function should be apply.

Article Details

How to Cite
1.
Chinsuwan N, Nitisiri K, Pinitkarn N, Radpukdee T, Sookkumnerd C. Poultry house climate control using a Fuzzy Logic Controller. featkku [internet]. 2020 Dec. 7 [cited 2026 Jan. 24];6(2):137-46. available from: https://ph02.tci-thaijo.org/index.php/featkku/article/view/240398
Section
Research Articles

References

Dawkins MS. Animal welfare and efficient farming: is conflict inevitable?. Animal Production Science 2017; 57(2): 201-8.

He SP, Arowolo MA, Medrano RF, Li S, Yu QF, Chen JY, et al. Impact of heat stress and nutritional interventions on poultry production. World's Poultry Science Journal 2018; 74(4): 647-64.

Zadeh LA. Fuzzy sets. Information and Control(1965); 8(3): 338-53.

Zadeh LA. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics 1973; 1: 28-44.

Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 1975; 7: 1.

Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 1975; 7: 1.

Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences 1975; 8(3): 199-249.

Chen T, Shang C, Su P, Shen Q. Induction of accurate and interpretable fuzzy rules from preliminary crisp representation. Knowledge-based Systems 2018; 146: 152-66.

King PJ, Mamdani EH. The application of fuzzy control systems to industrial processes. Automatica 1977; 13(3): 235-42.

Kamis MS, Abdullah AH, Sudin S, Shukor SAA, Bakar MAA, Mustafa MH. Closed house chicken barn climate control using fuzzy inference system. The Journal of Telecommunication, Electronic and Computer Engineering 2018; 10(1-14): 47-51.

Kolokotsa D. Comparison of the performance of fuzzy controllers for the management of the indoor environment. Building and Environment 2003; 38(12): 1439-50.

Mirzaee-Ghaleh E, Omid M, Keyhani A, Dalvand MJ. Comparison of fuzzy and on/off controllers for winter season indoor climate management in a model poultry house. Computers and Electronics in Agriculture 2015; 110: 187-95.

Upachaban T, Khongsatit K, Radpukdee T. Mathematical model and simulation study of a closed-poultry house environment. International Journal of Technology 2016; 7: 1117-23.