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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Indonesian Journal of Informatic Research and Software Engineering
Sari, Sitri Permata
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Algorithm Design and Programming the Luenberger Observer for level estimation in a Storage Tank System Mursyitah, Dian; Faizal, Ahmad; Sari, Sitri Permata
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 5 No. 1 (2025): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v5i1.2065

Abstract

This study presents the design and implementation of a Luenberger Observer algorithm for state estimation in a liquid storage tank system. The methodology includes system parameter identification, data preprocessing, observer gain calculation using pole placement, and simulation in MATLAB and Simulink. To reflect real-world conditions, synthetic disturbances were added and the input signal was normalized to improve numerical stability. Quantitative evaluation was conducted by comparing the system output with the observer’s estimated output. Simulation results demonstrate that the observer effectively tracks the system dynamics, yielding a root mean square error (RMSE) of 7×10?? m and a near-zero steady-state error. The observer's robustness was also tested systematically through increasing levels of synthetic measurement noise, showing stable and accurate performance even under 6% noise conditions. These findings confirm that the proposed algorithm provides reliable and responsive state estimation, with strong potential for practical application in control systems for dynamic fluid environments
Fault Detection In Storage Tank System Using Luenbeger Observer (LO): Simulation-Based Validation. Mursyitah, Dian; Faizal, Ahmad; Zarory, Hilman; Sari, Sitri Permata
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 3: November 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n3.1332.2025

Abstract

This study presents a comprehensive, simulation-based validation of a Luenberger Observer (LO) specifically designed for fault detection in storage tank systems. It commences with the development of a nonlinear storage tank model, which is subsequently linearized to streamline the observer design process. The LO estimates critical system states and produces residual signals that enable reliable fault detection. The observer gain is meticulously chosen using pole placement techniques to ensure rapid convergence of estimates and overall stability. To evaluate the effectiveness of this approach, three distinct fault scenarios—ramp, square pulse, and inverted ramp signals—are introduced to simulate various types of abnormal conditions that could occur in real-world operations. Simulation results demonstrate that the LO accurately estimates the liquid level states with a mean absolute error of approximately 0.02 meters, equivalent to about 2.6%. Furthermore, the observer detects faults with an average delay between 5 and 9 seconds following fault injection, indicating its prompt response capability. Notably, even with sensor noise levels reaching 6%, the observer maintains stable tracking performance, demonstrating strong robustness against disturbances. Across all tested scenarios, the residual signals show rapid increases during fault conditions and swiftly return near zero once the system reverts to normal operation, with no false alarms observed. Collectively, these results suggest that the Luenberger Observer provides an accurate, rapid, and disturbance-tolerant method for fault detection in storage tank systems. Such an approach offers a practical alternative to data-driven fault detection methodologies, as it relies less on extensive training datasets and can be more readily implemented for real-time industrial monitoring applications.
Co-Authors Ahmad Faizal Dian Mursyitah Zarory, Hilman