Optimal PID Controller Design for Liquid Level Tank via Modified Artificial Hummingbird Algorithm

Yıl 2023, , 37 - 43, 18.10.2023

Erdal Eker , Serdar Ekinci , Davut İzci

https://doi.org/10.53070/bbd.1346269

Cited By: 3

Öz

To enhance controller performance, the optimization of control parameters has emerged as a critical research area. Among the array of optimization algorithms, the modified elite opposition-based artificial hummingbird algorithm (m-AHA) stands out for its ability to emulate behavioral strategies of hummingbirds and elite opposition-based technique. This paper, therefore, proposes m-AHA optimizer as a novel approach to optimize control parameters in a three-tanks liquid level system. By fine-tuning the parameters of proportional-integral-derivative (PID) controller, superior performance is achieved. Comparative evaluations with competitive algorithms, including the arithmetic optimization algorithm with Harris hawks optimization and covariance matrix adaptation evolution strategy, assess the m-AHA optimizer-based approach for three-tank liquid level system control. The ITAE (integral of time multiplied absolute error) performance index analyzes time domain and frequency metrics, revealing the outstanding performance of the m-AHA optimizer-based approach.

Anahtar Kelimeler

Artificial hummingbird algorithm, Liquid level system, Controller design, Optimization

Kaynakça

• Abualigah, L., Ekinci, S., Izci, D., & Abu Zitar, R. (2023). Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System. Intelligent Automation & Soft Computing. https://doi.org/10.32604/iasc.2023.040291
• Amuthambigaiyin Sundari, K., & Maruthupandi, P. (2022). Optimal Design of PID Controller for the analysis of Two TANK System Using Metaheuristic Optimization Algorithm. Journal of Electrical Engineering & Technology, 17(1), 627–640. https://doi.org/10.1007/s42835-021-00891-6
• Bhookya, J., Vijaya Kumar, M., Ravi Kumar, J., & Seshagiri Rao, A. (2022). Implementation of PID controller for liquid level system using mGWO and integration of IoT application. Journal of Industrial Information Integration, 28, 100368. https://doi.org/10.1016/j.jii.2022.100368
• Ekinci, S., Izci, D., Abu Zitar, R., Alsoud, A. R., & Abualigah, L. (2022). Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems. Neural Computing and Applications, 34(22), 20263–20283. https://doi.org/10.1007/s00521-022-07575-w
• Ekinci, S., Izci, D., Eker, E., Abualigah, L., Thanh, C.-L., & Khatir, S. (2023). Hunger games pattern search with elite opposite-based solution for solving complex engineering design problems. Evolving Systems. https://doi.org/10.1007/s12530-023-09526-9
• Ekinci, S., Izci, D., & Hekimoglu, B. (2021). Implementing the Henry Gas Solubility Optimization Algorithm for Optimal Power System Stabilizer Design. Electrica, 21(2), 250–258. https://doi.org/10.5152/electrica.2021.20088
• Ekinci, S., Izci, D., & Kayri, M. (2023). Artificial hummingbird optimizer as a novel adaptive algorithm for identifying optimal coefficients of digital IIR filtering systems. International Journal of Modelling and Simulation, 1–15. https://doi.org/10.1080/02286203.2023.2240564
• Issa, M. (2022). Parameter Tuning of PID Controller Based on Arithmetic Optimization Algorithm in IOT Systems (pp. 399–417). https://doi.org/10.1007/978-3-030-99079-4_15
• Issa, M. (2023). Enhanced Arithmetic Optimization Algorithm for Parameter Estimation of PID Controller. Arabian Journal for Science and Engineering, 48(2), 2191–2205. https://doi.org/10.1007/s13369-022-07136-2
• Izci, D., & Ekinci, S. (2021). Comparative Performance Analysis of Slime Mould Algorithm For Efficient Design of Proportional–Integral–Derivative Controller. Electrica, 21(1), 151–159. https://doi.org/10.5152/electrica.2021.20077
• Izci, D., Ekinci, S., & Hussien, A. G. (2023). Effective PID controller design using a novel hybrid algorithm for high order systems. PLOS ONE, 18(5), e0286060. https://doi.org/10.1371/journal.pone.0286060
• Kıymaç, E., & Kaya, Y. (2023). A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm. Expert Systems with Applications, 213, 119162. https://doi.org/10.1016/j.eswa.2022.119162
• Moharam, A., El-Hosseini, M. A., & Ali, H. A. (2016). Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers. Applied Soft Computing, 38, 727–737. https://doi.org/10.1016/j.asoc.2015.10.041
• Snášel, V., Rizk-Allah, R. M., Izci, D., & Ekinci, S. (2023). Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer. Applied Soft Computing, 136, 110085. https://doi.org/10.1016/j.asoc.2023.110085
• Stefanoiu, D., & Culita, J. (2021). Optimal Identification and Metaheuristic PID Control of a Two-Tank System. Electronics, 10(9), 1101. https://doi.org/10.3390/electronics10091101
• Yildiz, B. S., Mehta, P., Sait, S. M., Panagant, N., Kumar, S., & Yildiz, A. R. (2022). A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems. Materials Testing, 64(7), 1043–1050. https://doi.org/10.1515/mt-2022-0123
• Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194. https://doi.org/10.1016/j.cma.2021.114194