Optimasi Thermal Oil Heater Menggunakan PSO Sebagai Tunning PID Controller
DOI:
https://doi.org/10.32492/nucleus.v1i2.43Keywords:
Thermal Oil Heater, PSO, PID ControllerAbstract
Optimasi Optimization of the auto temperature control system on the thermal oil heater system using PSO as a PID Controller tunning. Making a PSO-based Simulink tuning PID controller for thermal oil heater temperature in the 2013a Matlab program. Thermal oil heater simulation using PSO as a PID Controller tunning is the best result among other design methods. With kp = 2,057, ki = 1.337, kd = 0.148, we get an overshot value of = 0.002, undershot = 0, at a settling time of 4.521 seconds. This shows that the PID-PSO controller is the best method with the smallest overshot at 0.002, the smallest undershot at 0, and the fastest settling time at 4.521 seconds.
References
M. Ali, A. Raikhani, B. Budiman, and H. Sopian, “Algoritma Persaingan Imperialis Sebagai Optimasi Kontroler PID dan ANFIS Pada Mesin Sinkron Magnet Permanen,” JEEE-U (Journal Electr. Electron. Eng., vol. 3, no. 1, pp. 57–81, Apr. 2019, doi: 10.21070/jeee-u.v3i1.2023.
Rukslin and M. Ali, “Desain Pitch Angle dengan Tunning Bat Algorithm (BA) pada Wind Turbine Menggunakan PID Controller,” JEEE-U (Journal Electr. Electron. Eng., vol. 6, no. 1, pp. 40–51, Apr. 2022, doi: 10.21070/jeeeu.v6i1.1624.
M. Ali, M. A. Haikal, R. Rukslin, and H. Nurohmah, “Optimisasi Steering Control Pada Mobil Listrik Auto-Pilot Menggunakan Metode Bat Algorithm,” JE-Unisla, vol. 7, no. 1, p. 36, Apr. 2022, doi: 10.30736/je-unisla.v7i1.813.
M. Ali, H. Suyono, M. A. Muslim, M. R. Djalal, Y. M. Safarudin, and A. A. Firdaus, “Determination of the parameters of the firefly method for PID parameters in solar panel applications,” SINERGI, vol. 26, no. 2, p. 265, Jun. 2022, doi: 10.22441/sinergi.2022.2.016.
M. Ali and M. Ulum, “Perbandingan Optimasi Kontroler Putaran Motor Permanent Magnet Syschronous Machine,” J. FORTECH, vol. 1, no. 1, pp. 12–19, Feb. 2020, doi: 10.32492/fortech.v1i1.218.
Machrus Ali, H. Nurohmah, Rukslin, Dwi Ajiatmo, and M Agil Haikal, “Hybrid Design Optimization of Heating Furnace Temperature using ANFIS-PSO,” J. FORTEI-JEERI, vol. 1, no. 2, pp. 35–42, Dec. 2020, doi: 10.46962/forteijeeri.v1i2.21.
Machrus Ali, Ruslan Hidayat, and Iwan Cahyono, “Penggunaan ANFIS pada Pengaturan Debit Air Berdasarkan Volume Air Dalam Tangki,” ALINIER J. Artif. Intell. Appl., vol. 1, no. 1, pp. 24–32, Mar. 2020, doi: 10.36040/alinier.v1i1.2519.
M. Ali, A. Raikhani, B. Budiman, and H. Sopian, “Algoritma Persaingan Imperialis Sebagai Optimasi Kontroler PID dan ANFIS Pada Mesin Sinkron Magnet Permanen,” JEEE-U (Journal Electr. Electron. Eng., vol. 3, no. 1, pp. 57–81, Apr. 2019, doi: 10.21070/jeee-u.v3i1.2023.
Muhammad Agil Haikal, Dandy Tulus Herlambang, Machrus Ali, and Muhlasin, “Desain Optimasi PID Controller Pada Heating Furnace Temperature Menggunakan Metode Particle Swarm Optimization (PSO),” ALINIER J. Artif. Intell. Appl., vol. 2, no. 2, pp. 77–82, Nov. 2021, doi: 10.36040/alinier.v2i2.5162.
M. Ali, Muhlasin, H. Nurohmah, A. Raikhani, H. Sopian, and N. Sutantra, “Combined ANFIS method with FA, PSO, and ICA as Steering Control Optimization on Electric Car,” in 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Oct. 2018, pp. 299–304. doi: 10.1109/EECCIS.2018.8692885.
M. Ali, H. Suyono, M. A. Muslim, M. R. Djalal, Y. M. Safarudin, and A. A. Firdaus, “Determination of the parameters of the firefly method for PID parameters in solar panel applications,” SINERGI, vol. 26, no. 2, p. 265, Jun. 2022, doi: 10.22441/sinergi.2022.2.016.
A. Parwanti, S. I. Wahyudi, M. F. Ni’Am, M. Ali, Iswinarti, and M. A. Haikal, “Modified Firefly Algorithm for Optimization of the Water Level in the Tank,” in 2021 3rd International Conference on Research and Academic Community Services (ICRACOS), Oct. 2021, pp. 113–116. doi: 10.1109/ICRACOS53680.2021.9701981.
Budiman and M. Ali, “Modifikasi Firefly Algorithm Untuk Partial Shading pada Photovoltaic,” ALINIER J. Artif. Intell. Appl., vol. 2, no. 1, pp. 22–27, May 2021, doi: 10.36040/alinier.v2i1.3534.
Ardi Junianto, Machrus Ali, and Rukslin, “Hybrid Adaptive-Neuro-Fuzzy-Inference-System – Bat-Algorithms (ANFIS-BA) Pada Pengaturan Kecepatan Motor Induksi,” J. Intake J. Penelit. Ilmu Tek. dan Terap., vol. 12, no. 1, pp. 45–52, Apr. 2021, doi: 10.48056/jintake.v12i1.176.
M. Hasib Al Isbilly, Markhaban Siswanto, and Machrus Ali, “Optimasi PID Kontroller Pada Sistem Pengaturan Irigasi Menggunakan Metode Bat Algorithm,” J. JEETech, vol. 3, no. 2, pp. 78–83, Oct. 2022, doi: 10.48056/jeetech.v3i2.198.
Y. G. Hartlambang, H. Nurohmah, and M. Ali, “Optimasi Kecepatan Motor DC Menggunakan Algoritma Kelelawar (Bat Algorithm),” in SEMANTIKOM 2017, Universitas Madura, 2017, pp. 1–8. [Online]. Available: http://semantikom.unira.ac.id/2017/SEMANTIKOM_2017_paper_2.pdf
Y. G. Hartlambang, H. Nurohmah, and M. Ali, “Optimasi Kecepatan Motor DC Menggunakan Algoritma Kelelawar (Bat Algorithm),” Semant. 2017, Univ. Madura, no. 1, pp. 1–8, 2017, [Online]. Available: http://semantikom.unira.ac.id/2017/SEMANTIKOM_2017_paper_2.pdf
M. Ali, R. Rukslin, and C. Hasyim, “Hybrid System of Dual Axis Photovoltaic Tracking System Using Pid-Ces-Aco,” JEEMECS (Journal Electr. …, vol. 4, no. 2, pp. 59–68, 2021.
M. Ali and H. Sucipto, “Ant Colony Optimization Algorithm Implementation for Distribution of Natural Disaster Relief Logistics in Jombang Regency Web Base,” IOP Conf. Ser. Earth Environ. Sci., vol. 704, no. 1, p. 12008, Mar. 2021, doi: 10.1088/1755-1315/704/1/012008.
M. Ali, A. N. Afandi, A. Parwati, R. Hidayat, and C. Hasyim, “DESIGN OF WATER LEVEL CONTROL SYSTEMS USING PID AND ANFIS BASED ON FIREFLY ALGORITHM,” JEEMECS (Journal Electr. Eng. Mechatron. Comput. Sci., vol. 2, no. 1, Feb. 2019, doi: 10.26905/jeemecs.v2i1.2804.
Kadaryono, Rukslin, M. Ali, Askan, A. Parwanti, and I. Cahyono, “Comparison of LFC Optimization on Micro-hydro using PID, CES, and SMES based Firefly Algorithm,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Oct. 2018, pp. 204–209. doi: 10.1109/EECSI.2018.8752733.
