Advanced control parameter optimization in DC motors and liquid level systems
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. Am...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-85273-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544717586661376 |
|---|---|
| author | Serdar Ekinci Davut Izci Mohammad H. Almomani Kashif Saleem Raed Abu Zitar Aseel Smerat Vaclav Snasel Absalom E. Ezugwu Laith Abualigah |
| author_facet | Serdar Ekinci Davut Izci Mohammad H. Almomani Kashif Saleem Raed Abu Zitar Aseel Smerat Vaclav Snasel Absalom E. Ezugwu Laith Abualigah |
| author_sort | Serdar Ekinci |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-110aa5f17ee9445c87a58b037da2ee26 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-110aa5f17ee9445c87a58b037da2ee262025-01-12T12:19:52ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85273-yAdvanced control parameter optimization in DC motors and liquid level systemsSerdar Ekinci0Davut Izci1Mohammad H. Almomani2Kashif Saleem3Raed Abu Zitar4Aseel Smerat5Vaclav Snasel6Absalom E. Ezugwu7Laith Abualigah8Department of Computer Engineering, Batman UniversityDepartment of Computer Engineering, Batman UniversityDepartment of Mathematics, Facility of Science, The Hashemite UniversityDepartment of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud UniversityFaculty of Engineering and Computing, Liwa CollegeFaculty of Educational Sciences, Al-Ahliyya Amman UniversityFaculty of Electrical Engineering and Computer Science, VŠB-Technical University of OstravaUnit for Data Science and Computing, North-West UniversityComputer Science Department, Al al-Bayt UniversityAbstract 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.https://doi.org/10.1038/s41598-025-85273-yMountain Gazelle optimizerPID controllerParameter estimationDC motor speed regulationLiquid level control |
| spellingShingle | Serdar Ekinci Davut Izci Mohammad H. Almomani Kashif Saleem Raed Abu Zitar Aseel Smerat Vaclav Snasel Absalom E. Ezugwu Laith Abualigah Advanced control parameter optimization in DC motors and liquid level systems Scientific Reports Mountain Gazelle optimizer PID controller Parameter estimation DC motor speed regulation Liquid level control |
| title | Advanced control parameter optimization in DC motors and liquid level systems |
| title_full | Advanced control parameter optimization in DC motors and liquid level systems |
| title_fullStr | Advanced control parameter optimization in DC motors and liquid level systems |
| title_full_unstemmed | Advanced control parameter optimization in DC motors and liquid level systems |
| title_short | Advanced control parameter optimization in DC motors and liquid level systems |
| title_sort | advanced control parameter optimization in dc motors and liquid level systems |
| topic | Mountain Gazelle optimizer PID controller Parameter estimation DC motor speed regulation Liquid level control |
| url | https://doi.org/10.1038/s41598-025-85273-y |
| work_keys_str_mv | AT serdarekinci advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT davutizci advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT mohammadhalmomani advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT kashifsaleem advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT raedabuzitar advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT aseelsmerat advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT vaclavsnasel advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT absalomeezugwu advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems AT laithabualigah advancedcontrolparameteroptimizationindcmotorsandliquidlevelsystems |