Application of Ant Colony Optimization Algorithm in Determining PID Parameters in AC Motor Control
DOI:
10.47709/brilliance.v4i2.4741Keywords:
Ant Colony Optimization, PID Controller, Motor, Matlab, Parameter OptimizationDimension Badge Record
Abstract
Application of Ant Colony Optimization (ACO) Algorithm in determining PID (Proportional-Integral-Derivative) parameters to optimize AC motor control through simulation using MATLAB. AC motors are a critical component in a wide range of industrial applications requiring efficient control to ensure optimal stability and response. This research focuses on optimizing the motor's RPM control by fine-tuning PID parameters using the ACO algorithm. Precise RPM control is crucial for maintaining performance in dynamic industrial environments. The ACO algorithm is used to optimize the PID parameter by referring to the objective function of Integral Time Absolute Error (ITAE). The optimization results show that this algorithm can achieve optimal convergence in the 33rd iteration with a fitness value of 6269. The optimal PID parameters obtained were Kp of 164.98, Ki of 23.47, and Kd of 10.51. The simulation of the AC motor control system shows a significant improvement in performance compared to the Trial-and-Error method. The simulation results demonstrate that ACO reduces steady-state errors by up to 9%, while Trial-and-Error reaches 25%. The settling time is also faster with ACO, which is 0.7 seconds, compared to the Trial-and-Error method which takes longer. The use of the ACO method in PID tuning has been proven to be more efficient and accurate than conventional approaches, thus improving the RPM stability and response of the AC motor control system. This study concludes that the integration between ACO and PID can be the optimal solution in automated control applications in industries that require responsive and stable motor RPM control.
Abstract viewed = 50 times
References
Benny Prastikha Hadhi, Herlambang Setiadi, I. R. (2013). Optimisasi Pengaturan Frekuensi Sistem Interarea Menggunakan Algoritma Particle Swarm Optimization ( PSO ) dan Ant Colony. Seminar on Itellligent Technology And Its Aplplication, September 2016.
Chen, G., Li, Z., Zhang, Z., & Li, S. (2020). An Improved ACO Algorithm Optimized Fuzzy PID Controller for Load Frequency Control in Multi Area Interconnected Power Systems. IEEE Access, 8, 6429–6447. https://doi.org/10.1109/ACCESS.2019.2960380
Diantoro, S. (2024). Simulasi dan optimasi efisiensi motor induksi tiga fasa dengan variasi frekuensi menggunakan matlab simulink.
Fallo, D. Y. (2018). Pencarian Jalur Terpendek Menggunakan Algoritma Ant Colony Optimization. Jurnal Pendidikan Teknologi Informasi (JUKANTI), 1(1), 28–32. https://doi.org/10.37792/jukanti.v1i1.8
Harahap, C. R. (2022). Sistem Pengendalian Kecepatan Dua Motor Brushless DC (BLDC) dengan Nine Switch Inverter Menggunakan Metode PWM. Electrician, 16(3), 338–345. https://doi.org/10.23960/elc.v16n3.2388
Herlambang, T., Rahmalia, D., & Yulianto, T. (2019). Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system. Journal of Physics: Conference Series, 1211(1). https://doi.org/10.1088/1742-6596/1211/1/012039
Karyanti, W. N., Nawawi, I., & ... (2022). Sistem Kendali Motor Induksi 3 Fasa Dengan Inverter Topologi Dioda Clamped 5 Level Berbasis Pid. … " Seminar Nasional Riset …. https://jurnal.untidar.ac.id/index.php/senaster/article/view/5410
Ma’Arif, A., Nabila, H., Iswanto, & Wahyunggoro, O. (2019). Application of Intelligent Search Algorithms in Proportional-Integral-Derivative Control of Direct-Current Motor System. Journal of Physics: Conference Series, 1373(1). https://doi.org/10.1088/1742-6596/1373/1/012039
Mahfoud, S., Derouich, A., El Ouanjli, N., Quynh, N. V., & Mossa, M. A. (2022). A New Hybrid Ant Colony Optimization Based PID of the Direct Torque Control for a Doubly Fed Induction Motor. World Electric Vehicle Journal, 13(5). https://doi.org/10.3390/wevj13050078
Nurlaelasari, E., Supriyadi, S., & Lenggana, U. T. (2018). Penerapan Algoritma Ant Colony Optimization Menentukan Nilai Optimal Dalam Memilih Objek Wisata Berbasis Android. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 287–298. https://doi.org/10.24176/simet.v9i1.1914
Priyambodo, T. K., Dharmawan, A., Dhewa, O. A., & Putro, N. A. S. (2016). Optimizing control based on fine tune PID using ant colony logic for vertical moving control of UAV system. AIP Conference Proceedings, 1755. https://doi.org/10.1063/1.4958613
Ramadhan, N. R. (2024). Simulasi Kontrol PID Ziegler-Nichols pada Sistem Penghancuran Batu dengan Motor Induksi 3 Fasa 20HP. Jurnal Elektronika Dan Otomasi Industri, 11(1), 227–237. https://doi.org/10.33795/elkolind.v11i1.5146
Ruswandi Djalal, M. (2019). ANT COLONY BASED PID TUNED PARAMETERS FOR CONTROLLING SYNCHRONOUS MOTOR. Technology Acceptance Model, 10(1).
Ruswandi Djalal, M., & Rahmat. (2020). Penalaan optimal kendali motor DC berbasis ant colony optimization. Jurnal Teknologi, 12(1), 49–56. https://dx.doi.org/10.24853/jurtek.12.1.49-56
Sianturi, R. Y. C., Rahayudi, B., & Widodo, A. W. (2021). Implementasi Algoritma Ant Colony Optimization untuk Optimasi Rute Distribusi Produk Kebutuhan Pokok dari Toko Sasana Bonafide Mojoroto . Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(7), 3190–3197.
Udjulawa, D., & Oktarina, S. (2022). Penerapan Algoritma Ant Colony Optimization Untuk Pencarian Rute Terpendek Lokasi Wisata. Klik - Jurnal Ilmu Komputer, 3(1), 26–33. https://doi.org/10.56869/klik.v3i1.326
Wang, L., Luo, Y., & Yan, H. (2023). Ant colony optimization-based adjusted PID parameters: a proposed method. PeerJ Computer Science, 9, 1–17. https://doi.org/10.7717/peerj-cs.1660
Downloads
ARTICLE Published HISTORY
How to Cite
Issue
Section
License
Copyright (c) 2024 Farhan Wahyu Nur Rahman, Edy Setiawan, Anda Iviana Juniani, Anggara Trisna Nugraha
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.