Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control

Dlabač, Tatijana; Antić, Sanja; Ćalasan, Martin; Milovanovic, Alenka; Marvučić, Nikola

doi:10.3390/app13095414

Cited by 1 publication

(1 citation statement)

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“…Therefore, in the realm of industrial process control, the precise regulation of liquid levels in tanks is of utmost significance to ensure efficient and stable operations [2]- [4]. One of the most widely employed control techniques is the proportional-integral-derivative (PID) control system [5]- [7], which leverages a feedback loop to maintain desired liquid levels within the tanks [8]- [10]. However, despite its popularity, the traditional PID control may face challenges when it comes to optimizing complex and nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

Izci,

Ekinci

2023

IJRCS

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The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation.

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