Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm
Abstract This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37895-3 |
| _version_ | 1797779012409360384 |
|---|---|
| author | Muhammad I. Azeez A. M. M. Abdelhaleem S. Elnaggar Kamal A. F. Moustafa Khaled R. Atia |
| author_facet | Muhammad I. Azeez A. M. M. Abdelhaleem S. Elnaggar Kamal A. F. Moustafa Khaled R. Atia |
| author_sort | Muhammad I. Azeez |
| collection | DOAJ |
| description | Abstract This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications. |
| first_indexed | 2024-03-12T23:24:38Z |
| format | Article |
| id | doaj.art-dc2aee1db00046faa024228c34d087f6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T23:24:38Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dc2aee1db00046faa024228c34d087f62023-07-16T11:13:52ZengNature PortfolioScientific Reports2045-23222023-07-0113111910.1038/s41598-023-37895-3Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithmMuhammad I. Azeez0A. M. M. Abdelhaleem1S. Elnaggar2Kamal A. F. Moustafa3Khaled R. Atia4Mechanical Design and Production Engineering Department, Zagazig UniversityMechanical Design and Production Engineering Department, Zagazig UniversityMechanical Design and Production Engineering Department, Zagazig UniversityIndustrial Engineering Department, Zagazig UniversityMechanical Design and Production Engineering Department, Zagazig UniversityAbstract This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications.https://doi.org/10.1038/s41598-023-37895-3 |
| spellingShingle | Muhammad I. Azeez A. M. M. Abdelhaleem S. Elnaggar Kamal A. F. Moustafa Khaled R. Atia Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm Scientific Reports |
| title | Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm |
| title_full | Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm |
| title_fullStr | Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm |
| title_full_unstemmed | Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm |
| title_short | Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm |
| title_sort | optimization of pid trajectory tracking controller for a 3 dof robotic manipulator using enhanced artificial bee colony algorithm |
| url | https://doi.org/10.1038/s41598-023-37895-3 |
| work_keys_str_mv | AT muhammadiazeez optimizationofpidtrajectorytrackingcontrollerfora3dofroboticmanipulatorusingenhancedartificialbeecolonyalgorithm AT ammabdelhaleem optimizationofpidtrajectorytrackingcontrollerfora3dofroboticmanipulatorusingenhancedartificialbeecolonyalgorithm AT selnaggar optimizationofpidtrajectorytrackingcontrollerfora3dofroboticmanipulatorusingenhancedartificialbeecolonyalgorithm AT kamalafmoustafa optimizationofpidtrajectorytrackingcontrollerfora3dofroboticmanipulatorusingenhancedartificialbeecolonyalgorithm AT khaledratia optimizationofpidtrajectorytrackingcontrollerfora3dofroboticmanipulatorusingenhancedartificialbeecolonyalgorithm |