ArticleScientific reports2025
Advanced control parameter optimization in DC motors and liquid level systems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Neural Network-Based PID Parameter Estimation Using Black Kite Algorithm Hyperparameter Optimization for DC Motor Speed Control.Biomimetics (Basel, Switzerland) · 2026Article
- A novel hyperbolic tangent-based PID controller tuned by the artificial lemming algorithm for nonlinear steam condenser pressure control.Scientific reports · 2026Article
- A novel delay-affected two degree of freedom PID controller using physics-inspired optimization for robust control applications.Scientific reports · 2025Article
- Designing a cascaded exponential PID controller via starfish optimizer for DC motor and liquid level systems.Scientific reports · 2025Article
- Design of a novel and robust 2-DOF PIDA controller based on enzyme action optimizer for ball position regulation in magnetic levitation systems.Scientific reports · 2025Article
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Authors and funding
9 authors.
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Abstract
In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO's superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.
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