Pengendalian Level Air Modern dengan Algoritma Semut ACO-PID untuk Kontrol Presisi
DOI:
https://doi.org/10.56795/fortech.v4i2.4207Keywords:
Ant Colony Optimization, Ketinggian air, PID Kontroler, Penalaan Parameter, Settling TimeAbstract
Fluid flow measurement and control are fundamental requirements in various industrial processes, particularly for maintaining stable water volumes in storage tanks. Water level control systems typically employ Proportional–Integral–Derivative (PID) controllers. A primary challenge with PID controllers lies in determining the Kp, Ki, and Kd constants; manual tuning or trial-and-error approaches often result in slow, oscillatory, and suboptimal responses. This study proposes automated PID parameter tuning using Ant Colony Optimization (ACO), an artificial intelligence method that mimics the behavior of ant colonies in finding the shortest path via pheromone trails. The tuning objective function utilizes the Integral of Time-multiplied Absolute Error (ITAE) to minimize overshoot and settling time. The performance of the proposed PID-ACO method was compared against four scenarios—an uncontrolled system, conventional PID, Fuzzy Logic Controller (FLC), and Fuzzy-PID—using MATLAB/Simulink simulations. Simulation results demonstrate that PID-ACO delivers the best performance, achieving the lowest water level overshoot (0.5039 pu) and undershoot (0.5187 pu). Regarding output flow, the method achieved the lowest overshoot (0.0016 pu) and undershoot (0.0009 pu), as well as the fastest settling time. Thus, ACO-based PID tuning is proven to significantly enhance the stability and accuracy of water level control compared to the benchmark methods.
References
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, 2022, doi: 10.48056/jeetech.v3i2.198.
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, 2019, doi: 10.26905/jeemecs.v2i1.2804.
M. Siswanto, S. Arfaah, R. Rukslin, M. Muhlasin, and M. Ali, “Rekonfigurasi 33 Kanal Irigasi Menggunakan Metode Firefly Algorithm (MFA),” J. FORTECH, vol. 4, no. 1, pp. 43–47, 2023, doi: 10.56795/fortech.v4i1.4106.
I. Anshoruddin, M. Ali, R. Rukslin, and H. Nurohmah, “Desain Kontrol Pembangkit Listrik Tenaga Pikohidro Menggunakan PID-CES Berbasis Firefly Algorithm,” J. FORTECH, vol. 5, no. 2, pp. 89–94, 2024, doi: 10.56795/fortech.v5i2.5205.
B. Budiman and M. Ali, “PID Controller Design for Heating Furnace Temperature Based on Bat Algorithm (BA),” JEEMECS (Journal Electr. Eng. Mechatron. Comput. Sci., vol. 6, no. 1, pp. 45–50, Feb. 2023, doi: 10.26905/jeemecs.v6i1.9307.
M. Ali Fikri Haiqal, Rukslin, D. Ajiatmo, and M. Ali, “Optimasi Thermal Oil Heater Menggunakan ACO Sebagai Tunning PID Controller,” Nucl. J., vol. 2, no. 1, pp. 1–11, May 2023, doi: 10.32492/nucleus.v2i1.2101.
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, 2021, doi: 10.36040/alinier.v2i2.5162.
O. Maroufi, A. Choucha, and L. Chaib, “Hybrid fractional fuzzy PID design for MPPT-pitch control of wind turbine-based bat algorithm,” Electr. Eng., vol. 102, no. 4, pp. 2149–2160, 2020, doi: 10.1007/s00202-020-01007-5.
Hidayatul Nurohmah, M. Ali, Rukslin, Dwi Ajiatmo, and Muhammad Agil Haikal, “Komparasi PID, FLC, dan ANFIS sebagai Kontroller Dual Axis Tracking Photovoltaic berbasis Bat Algorithm,” J. JEETech, vol. 3, no. 2, pp. 71–77, 2022, doi: 10.48056/jeetech.v3i2.197.
M. Ali and M. Ulum, “Perbandingan Optimasi Kontroler Putaran Motor Permanent Magnet Syschronous Machine,” J. FORTECH, vol. 1, no. 1, pp. 12–19, 2020, doi: 10.32492/fortech.v1i1.218.
R. Nafiardli, S. Sunarto, M. Ali, and D. Ajiatmo, “Optimasi LFC (Load Frequency Control) Pada Mikrohidro Menggunakan Metode ACO-ANFIS dan BA-ANFIS,” Nucl. J., vol. 3, no. 1, pp. 29–38, May 2024, doi: 10.32492/nucleus.v3i1.3104.
M. Ali, A. A. Syaifudin, and H. Nurohmah, “Desain Hibrid Menggunakan PID-ANFIS Controller Pada Motor DC Berbasis PSO (Particle Swarm Optimization),” JE-Unisla, vol. 6, no. 2, p. 60, 2021, doi: 10.30736/je-unisla.v6i2.707.
S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems,” 2022. doi: 10.1016/j.heliyon.2022.e09399.
M. Ali et al., “The comparison of dual axis photovoltaic tracking system using artificial intelligence techniques,” IAES Int. J. Artif. Intell., vol. 10, no. 4, pp. 901–909, 2021, doi: 10.11591/IJAI.V10.I4.PP901-909.
M. H. Reza, K. Erwansyah, and L. Lusiyanti, “Monitoring Tangki Air Berbasis Internet Of Things,” J. Sist. Komput. Triguna Dharma (JURSIK TGD), vol. 2, no. 2, pp. 139–146, 2023, doi: 10.53513/jursik.v2i2.7370.
V. Kusuma Apsari, M. Ali, H. Nurohmah, and R. Rukslin, “Desain Optimasi PID Controller Pada Temperatur Heating Furnace Berbasis Ant Colony Algorithm (ACO),” J. FORTECH, vol. 2, no. 2, pp. 57–62, 2023, doi: 10.56795/fortech.v2i2.204.
M. Ali Fikri Haiqal, Rukslin, D. Ajiatmo, and M. Ali, “Optimasi Thermal Oil Heater Menggunakan ACO Sebagai Tunning PID Controller,” Nucl. J., vol. 2, no. 1, pp. 1–11, May 2023, doi: 10.32492/nucleus.v2i1.2101.
