Ekinci Serdar, Izci Davut, Almomani Mohammad H, Saleem Kashif, Zitar Raed Abu, Smerat Aseel, Snasel Vaclav, Ezugwu Absalom E, Abualigah Laith
Department of Computer Engineering, Batman University, Batman, 72100, Turkey.
Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
Sci Rep. 2025 Jan 9;15(1):1394. doi: 10.1038/s41598-025-85273-y.
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.
近年来，各工业领域的控制系统取得了显著进展，这就需要对动态系统进行有效管理以实现最佳功能。在优化控制参数以提高控制器性能方面，一个关键的研究重点已经出现。在众多优化算法中，山地瞪羚优化器（MGO）因其能够模拟山地瞪羚的敏捷运动和行为策略而脱颖而出。本文介绍了一种采用MGO优化直流电机和三容液位系统控制参数的新方法。使用MGO对比例积分微分（PID）控制器参数进行微调取得了显著成果，包括直流电机系统的上升时间为0.0478 s、无超调以及调节时间为0.0841 s。同样，液位系统的上升时间为11.0424 s，调节时间为60.6037 s，显示出控制效果得到改善。与灰狼优化器和粒子群优化等竞争算法的比较评估表明MGO具有卓越性能。此外，还引入了一个新的性能指标ZLG来全面评估控制质量。基于MGO的方法始终能实现更低的ZLG值，展示了其在动态系统控制和参数优化中的适应性和鲁棒性。通过提供一种可靠且高效的优化方法，本研究有助于推动控制系统的发展，促进不同工业应用中的稳定性并提高效率。
