Feedforward–Feedback Fuzzy-PID Water Level Control using PLC and Node-RED IoT
Water level control is vital in industrial processes to maintain operational stability and efficiency, especially against varying disturbances like changes in water inflow and outflow. This research proposes a combined feedforward–feedback control system using a Fuzzy-PID algorithm implemented on an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institut Teknologi Dirgantara Adisutjipto
2025-07-01
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| Series: | Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls |
| Subjects: | |
| Online Access: | https://ejournals.itda.ac.id/index.php/avitec/article/view/3066 |
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| Summary: | Water level control is vital in industrial processes to maintain operational stability and efficiency, especially against varying disturbances like changes in water inflow and outflow. This research proposes a combined feedforward–feedback control system using a Fuzzy-PID algorithm implemented on an Omron CP1H PLC, integrated with an IoT-based Node-RED monitoring interface. The system is designed to improve response accuracy and disturbance recovery in water level control applications. An experimental method was used to evaluate the performance of the proposed control system against conventional single-feedback control under varied disturbance scenarios. The results indicate that the combined control achieved a lower average steady-state error (0.67%) compared to feedback-only control (1.12%), faster recovery time (3 seconds vs. 6.3 seconds), and no overshoot. The integration of flow sensors as feedforward inputs enabled earlier detection and correction of disturbances before they impacted the water level. Additionally, the Node-RED interface allowed real-time monitoring and remote control, enhancing usability and supporting Industry 4.0 standards. While the system demonstrated improved stability and responsiveness, some oscillations remained due to sensor signal noise, suggesting a need for improved filtering techniques. This study contributes a practical and scalable solution for adaptive water level control, combining intelligent control strategies with IoT capabilities. It offers a foundation for future implementations in dynamic industrial environments that demand high reliability and remote accessibility. |
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| ISSN: | 2685-2381 2715-2626 |