{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:48:08Z","timestamp":1769881688271,"version":"3.49.0"},"reference-count":17,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study presents an advanced control system for liquid level regulation, comparing a traditional proportional-integral-derivative (PID) controller with a fuzzy logic controller. The system integrates a real-time monitoring and control interface, allowing flexible adjustments for research and training applications. Unlike the PID controller, which relies on predefined tuning parameters, the fuzzy logic controller dynamically adjusts control actions based on system behavior, making it more suitable for processes with non-linear dynamics. The experimental results highlight the superior performance of the fuzzy logic controller over the PID controller. Specifically, the fuzzy logic controller achieved a 21% reduction in maximum overshoot, a 62% decrease in peak time, and an 83% reduction in settling time. These improvements demonstrate its ability to handle process fluctuations more efficiently and respond rapidly to changes in liquid levels. By offering enhanced stability and adaptability, the fuzzy logic controller presents a viable alternative for liquid level control applications. Furthermore, this research contributes to the development of flexible and high-performance control solutions that can be implemented in both industrial and educational settings. The proposed system serves as a cost-effective platform for hands-on learning in control system design, reinforcing contemporary engineering education and advancing intelligent control strategies for industrial automation.<\/jats:p>","DOI":"10.3390\/computation13030060","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T09:04:49Z","timestamp":1740992689000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Comparative Study on Fuzzy Logic-Based Liquid Level Control Systems with Integrated Industrial Communication Technology"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1531-1824","authenticated-orcid":false,"given":"Hasan Mhd","family":"Nazha","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, Otto Von Guericke University Magdeburg, Universit\u00e4tsplatz 2, 39106 Magdeburg, Germany"}]},{"given":"Ali Mahmoud","family":"Youssef","sequence":"additional","affiliation":[{"name":"Faculty of Technical Engineering, University of Tartous, Tartous P.O. Box 2147, Syria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2027-7950","authenticated-orcid":false,"given":"Mohamad Ayham","family":"Darwich","sequence":"additional","affiliation":[{"name":"Faculty of Technical Engineering, University of Tartous, Tartous P.O. Box 2147, Syria"},{"name":"Faculty of Biomedical Engineering, Al-Andalus University for Medical Sciences, Tartous P.O. Box 101, Syria"}]},{"given":"Their Ahmad","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Faculty of Technical Engineering, University of Tartous, Tartous P.O. Box 2147, Syria"}]},{"given":"Hala Essa","family":"Homsieh","sequence":"additional","affiliation":[{"name":"Faculty of Technical Engineering, University of Tartous, Tartous P.O. Box 2147, Syria"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Owa, K.O., Sharma, S.K., and Sutton, R. (2013, January 4\u20135). Optimised Multivariable Nonlinear Predictive Control for Coupled Tank Applications. Proceedings of the IET Conference on Control and Automation 2013: Uniting Problems and Solutions, Birmingham, UK.","DOI":"10.1049\/cp.2013.0004"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roland, B. (2023). PID Parameter Tables after ITAE to Control Overshooting Systems Found with AI Algorithm. Int. J. Electr. Comput. Eng. Res.","DOI":"10.53375\/icmame.2023.216"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, V., Rana, K.P.S., Kumar, J., and Mishra, P. (2016). Self-Tuned Robust Fractional Order Fuzzy PD Controller for Uncertain and Nonlinear Active Suspension System. Neural Comput. Appl.","DOI":"10.1007\/s00521-016-2774-x"},{"key":"ref_4","first-page":"547","article-title":"Comparative Analysis of PID and Fuzzy Logic Controllers in Industrial Applications","volume":"20","author":"Singh","year":"2022","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_5","unstructured":"Smith, T., and Garcia, L. (2023). Advances in Intelligent Control Systems: A Review of Fuzzy Logic and AI-Based Controllers. Sensors, 23."},{"key":"ref_6","first-page":"312","article-title":"Implementation of Fuzzy Logic-Based Liquid Level Control in Chemical Processing Plants","volume":"186","author":"Kumar","year":"2024","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_7","first-page":"99","article-title":"Design and Experimental Validation of AI-Based Controllers for Process Automation","volume":"150","author":"Wong","year":"2023","journal-title":"J. Process Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Reddy, A.S., and Reddy, G.N. (2024). An Adaptive Fractional Order Controller Design: A Realization for Liquid Level Regulation in Liquid Level Plant. Meas. Sens., 31.","DOI":"10.1016\/j.measen.2023.100977"},{"key":"ref_9","unstructured":"Ayyagari, R., and Raol, J.R. (2019). 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Dev."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:45:43Z","timestamp":1760028343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,2]]},"references-count":17,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["computation13030060"],"URL":"https:\/\/doi.org\/10.3390\/computation13030060","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,2]]}}}