Constant force grinding controller for robots based on SAC optimal parameter finding algorithm
Abstract Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63384-2 |
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| _version_ | 1841544610155855872 |
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| author | Chosei Rei Qichao Wang Linlin Chen Xinhua Yan Peng Zhang Liwei Fu Chong Wang Xinghui Liu |
| author_facet | Chosei Rei Qichao Wang Linlin Chen Xinhua Yan Peng Zhang Liwei Fu Chong Wang Xinghui Liu |
| author_sort | Chosei Rei |
| collection | DOAJ |
| description | Abstract Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability. |
| format | Article |
| id | doaj-art-a7e84e459a534d80950f2d3401dda2d2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7e84e459a534d80950f2d3401dda2d22025-01-12T12:25:10ZengNature PortfolioScientific Reports2045-23222024-06-0114111510.1038/s41598-024-63384-2Constant force grinding controller for robots based on SAC optimal parameter finding algorithmChosei Rei0Qichao Wang1Linlin Chen2Xinhua Yan3Peng Zhang4Liwei Fu5Chong Wang6Xinghui Liu7Nobot Intelligent Equipment (Shandong) Co., LtdSchool of Mechanical and Automotive Engineering, Liaocheng UniversitySchool of Mechanical and Automotive Engineering, Liaocheng UniversityNobot Intelligent Equipment (Shandong) Co., LtdNobot Intelligent Equipment (Shandong) Co., LtdNobot Intelligent Equipment (Shandong) Co., LtdSchool of Mechanical and Automotive Engineering, Liaocheng UniversityDepartment of Materials Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMTS)Abstract Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.https://doi.org/10.1038/s41598-024-63384-2RobotsConstant force grindingSoft actor criticOptimal parameter finding algorithmSimulation model |
| spellingShingle | Chosei Rei Qichao Wang Linlin Chen Xinhua Yan Peng Zhang Liwei Fu Chong Wang Xinghui Liu Constant force grinding controller for robots based on SAC optimal parameter finding algorithm Scientific Reports Robots Constant force grinding Soft actor critic Optimal parameter finding algorithm Simulation model |
| title | Constant force grinding controller for robots based on SAC optimal parameter finding algorithm |
| title_full | Constant force grinding controller for robots based on SAC optimal parameter finding algorithm |
| title_fullStr | Constant force grinding controller for robots based on SAC optimal parameter finding algorithm |
| title_full_unstemmed | Constant force grinding controller for robots based on SAC optimal parameter finding algorithm |
| title_short | Constant force grinding controller for robots based on SAC optimal parameter finding algorithm |
| title_sort | constant force grinding controller for robots based on sac optimal parameter finding algorithm |
| topic | Robots Constant force grinding Soft actor critic Optimal parameter finding algorithm Simulation model |
| url | https://doi.org/10.1038/s41598-024-63384-2 |
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