Control of Non-Linear Liquid Level Using Fuzzy logic System
DOI:
https://doi.org/10.37375/susj.v16i1.4172Keywords:
Non-linear level system, PID controller, fuzzy logic system, set point trackingAbstract
Level control systems play a crucial role across a wide range of industries, including oil and gas processing, nuclear power plants, and water treatment facilities. While Proportional-Integral-Derivative (PID) controllers are commonly used in these systems, its effectiveness could be limited by challenges such as nonlinearity, system uncertainty, and time delays. To overcome these limitations, fuzzy logic control has appeared as a promising alternative. This paper investigates and compares the performance of traditional PID controllers with fuzzy logic controllers in handling non-linear level control systems. MATLAB software and Simulink library were used to model and simulate the system in different control scenarios. The initial results indicate that fuzzy logic controllers outperform PID controllers by offering faster response times, minimal overshoot, and eliminating steady-state errors. Additionally, the system’s response is evaluated under varying setpoint conditions to demonstrate adaptability.
References
Doan NT, Thanh PH. “LIQUID LEVEL STABILIZATION USING FUZZY ALGORITHM WITH PLC S7 1200”. Thu Dau Mot University Journal of Science.2025,7(1):93-101 .DOI: 10.37550/tdmu.EJS/2025.01.614
Nazha HM, Youssef AM, Darwich MA, Ibrahim TA, Homsieh HE. “A Comparative Study on Fuzzy Logic-Based Liquid Level Control Systems with Integrated Industrial Communication Technology”. Computation. 2025; 13(3):60. https://doi.org/10.3390/computation13030060
Imanov, E., Essa, M. (2024). Fuzzy Logic Controller Design for Liquid Level System. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 16th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2023. ICAFS 2023. Lecture Notes in Networks and Systems, vol 1141. Springer, Cham. https://doi.org/10.1007/978-3-031-76283-3_37
KASSIM SO, Ali AG, Harram IM. “Design And Implementation Of Mamdani Type Fuzzy Inference System Based Water Level Controller”. Journal of Electronics and Communication Engineering (IOSR-JECE) .Volume 16, Issue 4, Ser. I (Jul. – Aug. 2021), PP 15-22. DOI: 10.9790/2834-1604011522
Ashutha, K., Yadav, E.S., Indiran, T., & Shreesha, C. Implementation of Fuzzy Control for a Nonlinear System - Conical Level Process. In: 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE), Phuket, Thailand.
Bhandarel, D.S., & Kulkarni, N.R. Performance Evaluation and Comparison of PID Controller and Fuzzy Logic Controller for Process Liquid Level Control. In: International Conference on Control, Automation and Systems (ICCAS), 2015, Busan, Korea.
Vinothkumar, & Esakkiappan. Fuzzy PI and Fuzzy PID Controller Based Hopper Tank Level Control System. In: International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT).
Nathaniel, N. U., Hussein, S. U., Oshiga, O., Ali, N., Nzerem, P., & Thomas, S. (2023, November). Improved Liquid Level Control Design Using Mamdani Fuzzy Inference System. In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) (Vol. 1, pp. 1-7). IEEE. DOI: 10.1109/ICMEAS58693.2023.10429913
Ilyas, M., Shah, S. A. R., Rauf, A., Khalil, Y., & Ayaz, M. (2022). Stabilization of liquid level in a tank system based on fuzzy logic controller. IJRA (11), 4, 315.
Puviyarasi, B., Murukesh, C., & Srividya, K. (2022, March). Design and implementation of PID controller and fuzzy logic controller for liquid level system. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1463-1466). IEEE.
Mammadova, K., & Hasanquliyeva, M. (2023). Algorithms of Liquid Level Control Based on Fuzzy Logic Controllers. Available at SSRN 4662423. http://dx.doi.org/10.2139/ssrn.4662423
K. Husain, “Active Control of Surge Compressor System,” Sirte University Scientific Journal (SUSJ), vol. 9, no. 2, pp. 31–45, 2019.
Chau, P.C. Process Control: A First Course with MATLAB. 2002: Cambridge University Press.
Husain, K., & Khalifa, G. H. (2026). Liquid level control using a self-tuning PID controller based on fuzzy logic. International Science and Technology Journal, 38(1), 1–17. https://doi.org/10.62341/istj-vol38-2-66.
Dapke, S. G. (2025). Fuzzy Logic and Its Applications in Control Systems. Multidisciplinary Research Area in Arts, Science & Commerce (Volume-2), 86.
Al‐Hadithi, B. M., Adánez, J. M., & Jiménez, A. (2023). A multi‐strategy fuzzy control method based on the Takagi‐Sugeno model. Optimal Control Applications and Methods, 44(1), 91-109. https://doi.org/10.1002/oca.2932
Vidal-Martínez, R., García-Martínez, J. R., Rojas-Galván, R., Álvarez-Alvarado, J. M., Gozález-Lee, M., & Rodríguez-Reséndiz, J. (2025). A Review of Mamdani, Takagi–Sugeno, and Type-2 Fuzzy Controllers for MPPT and Power Management in Photovoltaic Systems. Technologies, 13(9), 422. https://doi.org/10.3390/technologies13090422
F. Alhaj Omar, “PERFORMANCE COMPARISON OF PID CONTROLLER AND FUZZY LOGIC CONTROLLER FOR WATER LEVEL CONTROL WITH APPLYING TIME DELAY”, KONJES, vol. 9, no. 4, pp. 858–871, 2021, doi: 10.36306/konjes.976918.
Saatchi, R. (2024). Fuzzy logic concepts, developments and implementation. Information, 15(10).
Ruspini, E., Bonissone, P., & Pedrycz, W. (Eds.). (2020). Handbook of fuzzy computation. CRC Press.
Nise, N.S. Control Systems Engineering. 2020: John Wiley & Sons.
Ogata, K. Modern Control Engineering (5th Edition). 2010.
