System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach
This study focuses on the crucial need for effective control strategies design for linear electromechanical actuators (EMA), which are essential components in industrial automation. While the PI controller is commonly used due to its simplicity and versatility in various control scenarios, its eff...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
ARQII Publication
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/113925/7/113925_%20System%20identification%20and%20control.pdf http://irep.iium.edu.my/113925/ https://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/620 |
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| Summary: | This study focuses on the crucial need for effective control strategies design for linear electromechanical actuators
(EMA), which are essential components in industrial automation. While the PI controller is commonly used due to its simplicity and versatility in various control scenarios, its effectiveness is limited by its complex tuning process, which requires significant time and effort to achieve the optimal performance. To address this issue, the paper focuses on employing metaheuristic approaches that are Spiral Dynamic Algorithm (SDA) and Artificial Bee Colony (ABC) to fine tune the PI parameters for controlling the position of EMA. The simulation results, implemented in MATLAB Simulink, show that PI-SDA and PI-ABC produce better performances with minimal steady-state error, reduced overshoot, faster settling time and rise time. Hardware in-the-loop (HIL) testing proves the effectiveness of the controller's performance in real-world scenarios. PI-SDA achieved a steady-state error of 0.0623, zero overshoot, faster rise time of 1.6956 s, and faster settling time of 7.2166 s. As for validation, PI-ABC showed similar results in HIL environment with a steady-state error of -0.0314, an overshoot of 5.0007%, a rise time of 1.3805 s, and a settling time of 9.1002 s. These results highlight the effectiveness of metaheuristic methods in real-world
implementation and outperform the traditional heuristic approaches. |
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