Perancangan dan Integrasi Kendali Kelembaban Pada Simulator Kandang Ayam Berbasis Jaringan Nirkabel Menggunakan Algoritma Fuzzy-PID
Keywords:
kendali fuzzy-PID, node nirkabel, kelembabanAbstract
Sistem brooding ayam komersil mayoritas mengggunakan kipas dan saluran udara sebagai sumber sirkulasi udara untuk mengatur kelembaban di ruangan sekitar. Proses pengendalian biasanya menggunakan kendali on–off dan dapat terjadi fluktuasi kelembaban yang tidak terkendali. Untuk mengatasi hal tersebut maka dirancang metode kendali berbasis Fuzzy Logic Controller yang di implementasikan untuk menghasilkan kendali kelembaban yang optimal dan adaptif. Algoritma kendali yang dirancang kemudian diimplementasikan pada sebuah simulator berupa simulator kandang ayam tersusun dari 2 buah node yang terhubung secara nirkabel. Node sensor akan mengolah data input menggunakan sensor suhu BME280 dan node aktuator akan mengolah data menggunakan algoritma kendali Fuzzy Logic Controller dan penguat proporsional, integral serta derivatif (fuzzy-PID). Pada penelitian juga dirancang model fungsi alih dari plant yang digunakan dalam simulasi untuk proses analisis dan perbandingan dengan hasil eksperimen. Berdasarkan hasil eksperimen dan simulasi tanggapan pengendalian, kendali fuzzy–PID mampu menghasilkan tanggapan transisi yang cepat dengan nilai rise time sebesar 54 detik, settling time sebesar 95 detik dan delay time sebesar 43 detik dibandingkan dengan kendali PID konvensional. Hasil tersebut menunjukan bahwa algoritma kendali fuzzy–PID lebih baik dari kendali PDI serta dapat mengatasi waktu tunda dan memperkecil deadband, serta menghasilkan penguataan yang bervariasi sesuai dengan batasan nilai Kp, Ki, dan Kd.
The majority of commercial chicken brooding systems use fans and exhaust as a source of air circulation to regulate humidity in the surrounding space. The control process usually uses on-off controls and uncontrolled humidity fluctuation can occur. To overcome this problem, a Fuzzy Logic Controller based control method is designed which is implemented to produce optimal and adaptive humidity control. The control algorithm designed is then implemented in a simulator in the form of a chicken coop simulator composed of 2 nodes that are connected wirelessly. The sensor nodes will process the input data using the BME280 temperature sensor and the actuator nodes will process the data using the Fuzzy Logic Controller control algorithm and proportional, integral, and derivative (fuzzy-PID) amplifiers. In this research, a transfer function model of the plant that is used in the simulation is also designed for the analysis process and comparison with the experimental results. Based on the experimental results and control response simulations, the fuzzy-PID control can produce a fast transition response with a rise time value of 54 seconds, a settling time of 95 seconds, and a delay time of 43 seconds compared to conventional PID controls. These results indicate that the fuzzy-PID control algorithm is better than the PDI control and can overcome the delay time and reduce the delay time, as well as the results that vary according to the value limits of Kp, Ki, and Kd.
Downloads
References
B. Wihardyanto, “Pengaruh Lama Periode Brooding dan Level Protein Ransum Terhadap Produksi Karkas Ayam Kedu Hitam Umur 10 Minggu,” in Applied Microbiology and Biotechnology, pp. 2071–2079, vol. 85, no. 1, 2014,
Makmur, “Perancangan Prototipe Kandang Ayam Broiler Closed House Untuk Kontrol Suhu Dan Kelembaban Berbasis Arduino Mega 2560,” 2018.
C. G. N. Putra, R. Maulana, and H. Fitriyah, “Otomasi Kandang Dalam Rangka Meminimalisir Heat Stress Pada Ayam Broiler Dengan Metode Naive Bayes,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, pp. 387–394, vol. 2, no. 1, 2018.
D. I. Saputra, A. Rohmat, A. Najmurrokhman, and Z. Fakhri, “Implementation of fuzzy inference system algorithm in brooding system simulator with the concept of IoT and wireless nodes Implementation of fuzzy inference system algorithm in brooding system simulator with the concept of IoT and wireless nodes,” in ICIEVE 2019, pp. 1–6, 2020.
A. M. O. Fini, M. B. Gogani, and M. Pourgholi, “Fuzzy gain scheduling of PID controller implemented on real time level control,” 2016.
A. Mehranzadeh, “A Novel-Scheduling Algorithm for Cloud Computing based on Fuzzy Logic,” pp. 28–31, vol. 5, no. 7, 2013.
N. Sutarna and S. Rahayu, “Metode Tuning Operating Range Fuzzy PID Controller pada Sistem Orde Tiga,” J. Tek. Elektro, pp. 33–39, vol. 12, no. 1, 2020.
D. I. Saputra, A. Najmurrokhman, and Z. Fakhri, “Skema Implementasi Fuzzy Inference System tipe Sugeno Sebagai Algoritma Pengendali Pada Sistem Pengamatan Berbasis IoT,” in Seminar Nasional Sains dan Teknologi 2019, pp. 1–12, 2019.
M. Irfal, T. Alfiansyah, D. I. Saputra, and C. Yusuf, “Skema Implementasi Pengendali Heater Kandang Ayam Broiler Berbasis Node Nirkabel Menggunakan Logika Fuzzy,” in Seminar Nasional Teknologi dan Riset Terapan, no. 1, pp. 22–29, 2019.
R. B. Mahale and S. S. Sonavane, “Smart Poultry Farm Monitoring Using IOT and Wireless Sensor Networks,” Int. J. Adv. Res. Comput. Sci., pp. 187–190, vol. 7, no. 3, 2016.
B. Sugiarto, “The Use of Fuzzy Logic for Data Classification in Sensor Node on Wireless Sensor and Actuator Network (WSAN) System,” in Makassar International Conference on Electrical engineering and Informatics (MICEEI), pp. 109–113, no. November, 2014.
S. H. Xiong, Z. Zhong, and Z. Xin, “PD-type open-closed-loop iterative learning control in the networked control system,” in Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016, pp. 5738–5744, 2016.
U. J. Shobrina, R. Primananda, and R. Maulana, “Analisis Kinerja Pengiriman Data Modul Transceiver NRF24l01 , Xbee dan Wifi ESP8266 Pada Wireless Sensor Network,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., pp. 1510–1517, vol. 2, no. 4, 2018.
F. S. Fahmi and D. I. Saputra, “Pengontrolan Bukaan Valve Pada Sistem Vessel Separator Dengan Metode Logika Fuzzy Berbasis Node Nirkabel,” in SENTER 2019: Seminar Nasional Teknik Elektro 2019, pp. 135–149, no. November 2019.
Y. Wang, H. Zou, J. Tao, and R. Zhang, “Predictive fuzzy PID control for temperature model of a heating furnace,” in Chinese Control Conference, CCC, pp. 4523–4527, 2017.
