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Brilliance: Research of Artificial Intelligence

brilliance
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Published by Information Technology and Science

ISSN : - EISSN : 28079035 DOI : https://doi.org/10.47709

Core Subject : Science, Education,

Decision Sciences, Operations Research & Management Mathematics Other

Brilliance: Research of Artificial Intelligence is The Scientific Journal. Brilliance is published twice in one year, namely in February, May and November. Brilliance aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about Artificial Intelligence. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and colleagues will review. Articles sent to the Brilliance may not be published elsewhere. The manuscript must follow the author guidelines provided by Brilliance and must be reviewed and edited. Brilliance is published by Information Technology and Science (ITScience), a Research Institute in Medan, North Sumatra, Indonesia.

Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer

Articles 601 Documents

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Performance Comparison of Fuzzy-PID and MPC for Cascade Water Tank Control Systems Yuli Mauliza; Ghiyalti Novilia; Ibnu Khaldun; Muhammad Azzahari; M. Basyir
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9541

Level and flow control systems are widely used in industrial processes, where poor control performance may cause process instability, energy inefficiency, and reduced product quality. This study aims to design and compare the performance of Model Predictive Control and Fuzzy-PID in a cascade level-flow control system based on models obtained through system identification. An experimental simulation approach was conducted, including system identification, process modeling, controller design, and performance evaluation. Black-box modeling was performed using bump test data processed in the MATLAB System Identification Toolbox, producing level and flow models with average fit estimations of 87.54% and 85.19%, respectively. The identified models were used to develop both controllers. MPC was designed with a prediction horizon of 40, a control horizon of 3, and a sampling time of 0.01 s, while Fuzzy-PID employed a fuzzy inference system to tune PID parameters based on error and change of error. Simulation results show that both controllers achieved the desired setpoint. However, MPC provided superior performance with no overshoot, a settling time of approximately 8 s, and a steady-state error of 0.0001. In contrast, Fuzzy-PID produced 24% overshoot, a settling time of 28 s, and oscillatory responses under disturbances. These results indicate that MPC is more effective and reliable for cascade level-flow control applications in industrial systems.

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Muhammad Khoiruddin Harahap

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Abstract

Home Page

OAI Link

Editorial Team

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Reviewer

Google Scholar

Contact Name Muhammad Khoiruddin Harahap

Contact Email choir.harahap@yahoo.com

Phone +6282251583783

Journal Mail Official publikasi@itscience.org

Editorial Address Medan