Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm
This study focuses on controlling a compliant gripper using least-squares support vector regression (LS-SVR) combined with a particle swarm optimization (PSO) algorithm. The compliant gripper is designed to grip small objects with high precision. However, repeated use can lead to reduced precision d...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2518962 |
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| author | Poonnapa Chaichudchaval Archawin Chaitrekal Nawin Sutthiprapa Dung-An Wang Teeranoot Chanthasopeephan |
| author_facet | Poonnapa Chaichudchaval Archawin Chaitrekal Nawin Sutthiprapa Dung-An Wang Teeranoot Chanthasopeephan |
| author_sort | Poonnapa Chaichudchaval |
| collection | DOAJ |
| description | This study focuses on controlling a compliant gripper using least-squares support vector regression (LS-SVR) combined with a particle swarm optimization (PSO) algorithm. The compliant gripper is designed to grip small objects with high precision. However, repeated use can lead to reduced precision due to the hysteresis inherent in the gripper’s mechanism. To address this, an algorithm developed to mitigate the effect of hysteresis is seen to improve control accuracy. The algorithm is further designed to control the end-effector position of the gripper. Simulation results show that applying LS-SVR with the PSO algorithm enhances gripping precision. After implementing the control algorithm, gripping displacement was compared across three configurations: no controller, a conventional proportional–integral (PI) controller, and the proposed LS-SVR with PSO. The root mean square error (RMSE) decreased significantly to 5.13 × 10−2 mm with the proposed controller, compared to 7.03 × 10−2 mm without a controller and 6.69 × 10−2 mm with PI control. These results demonstrate that the LS-SVR with PSO significantly improves the precision of the compliant gripper. |
| format | Article |
| id | doaj-art-87cfde78e2334f048df5a2d39b2ee502 |
| institution | OA Journals |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-87cfde78e2334f048df5a2d39b2ee5022025-08-20T02:36:07ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2025.2518962Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithmPoonnapa Chaichudchaval0Archawin Chaitrekal1Nawin Sutthiprapa2Dung-An Wang3Teeranoot Chanthasopeephan4Department of Mechanical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandDepartment of Mechanical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandDepartment of Mechanical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandGraduate Institute of Precision Engineering, National Chung Hsing University, Taichung, TaiwanDepartment of Mechanical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandThis study focuses on controlling a compliant gripper using least-squares support vector regression (LS-SVR) combined with a particle swarm optimization (PSO) algorithm. The compliant gripper is designed to grip small objects with high precision. However, repeated use can lead to reduced precision due to the hysteresis inherent in the gripper’s mechanism. To address this, an algorithm developed to mitigate the effect of hysteresis is seen to improve control accuracy. The algorithm is further designed to control the end-effector position of the gripper. Simulation results show that applying LS-SVR with the PSO algorithm enhances gripping precision. After implementing the control algorithm, gripping displacement was compared across three configurations: no controller, a conventional proportional–integral (PI) controller, and the proposed LS-SVR with PSO. The root mean square error (RMSE) decreased significantly to 5.13 × 10−2 mm with the proposed controller, compared to 7.03 × 10−2 mm without a controller and 6.69 × 10−2 mm with PI control. These results demonstrate that the LS-SVR with PSO significantly improves the precision of the compliant gripper.https://www.tandfonline.com/doi/10.1080/21642583.2025.2518962Compliant gripperhysteresisleast-squares support vector regressionparticle swarm optimization |
| spellingShingle | Poonnapa Chaichudchaval Archawin Chaitrekal Nawin Sutthiprapa Dung-An Wang Teeranoot Chanthasopeephan Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm Systems Science & Control Engineering Compliant gripper hysteresis least-squares support vector regression particle swarm optimization |
| title | Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm |
| title_full | Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm |
| title_fullStr | Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm |
| title_full_unstemmed | Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm |
| title_short | Control of a compliant gripper via least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm |
| title_sort | control of a compliant gripper via least squares support vector regression ls svr with particle swarm optimization pso algorithm |
| topic | Compliant gripper hysteresis least-squares support vector regression particle swarm optimization |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2025.2518962 |
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