Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF
Abstract Neural Network has been used in approximation of dead zone nonlinearity when modeling the manipulator control systems. However the existed method fail to minimize the possible input saturation effect and the NN mapping accuracy also be degraded, which leads to degrading in tracking precise...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10981-4 |
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| author | Yu Han Zhiyu Song Wenyu Yi Caixue Zhan |
| author_facet | Yu Han Zhiyu Song Wenyu Yi Caixue Zhan |
| author_sort | Yu Han |
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| description | Abstract Neural Network has been used in approximation of dead zone nonlinearity when modeling the manipulator control systems. However the existed method fail to minimize the possible input saturation effect and the NN mapping accuracy also be degraded, which leads to degrading in tracking precise and accuracy. To establish an accurate control model of the tea picking robot, an adaptive compensator of m-RBF (modified radial basis function neural network) and adaptive law were designed aimed at the nonlinearity units and dead zone. The closed-loop tracking error of the proposed control system is eventually going to be stable and bounded. A simulation was carried out with Simulink, which show that m-RBF provides a good approximation to characteristic of Dead zone nonlinearity, and that the control scheme based on m-RBF had a excellent and stable tracking accuracy. The tea picking experiment with six-axis manipulator verifies the effectiveness of the proposed algorithm, in which the proposed method got a higher score of 95.3, near two times that of traditional PID control methods. It can be drown that the m-RBF has a faster learning rate and can avoid local minima; the control scheme based on m-RBF has a excellent performance on control accuracy, robustness and self-adaption, which is especially appropriate for real-time control, like tea picking robot. |
| format | Article |
| id | doaj-art-dfed53fc258d45dea0da28a7f487e5f1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-dfed53fc258d45dea0da28a7f487e5f12025-08-24T11:28:24ZengNature PortfolioScientific Reports2045-23222025-08-0115111410.1038/s41598-025-10981-4Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBFYu Han0Zhiyu Song1Wenyu Yi2Caixue Zhan3School of Automation, Southeast UniversityMinistry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural MachanizationKey Laboratory of Agricultural Equipment Technology for Hilly and Mountainous Areas, Ministry of Agriculture and Rural AffairsMinistry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural MachanizationAbstract Neural Network has been used in approximation of dead zone nonlinearity when modeling the manipulator control systems. However the existed method fail to minimize the possible input saturation effect and the NN mapping accuracy also be degraded, which leads to degrading in tracking precise and accuracy. To establish an accurate control model of the tea picking robot, an adaptive compensator of m-RBF (modified radial basis function neural network) and adaptive law were designed aimed at the nonlinearity units and dead zone. The closed-loop tracking error of the proposed control system is eventually going to be stable and bounded. A simulation was carried out with Simulink, which show that m-RBF provides a good approximation to characteristic of Dead zone nonlinearity, and that the control scheme based on m-RBF had a excellent and stable tracking accuracy. The tea picking experiment with six-axis manipulator verifies the effectiveness of the proposed algorithm, in which the proposed method got a higher score of 95.3, near two times that of traditional PID control methods. It can be drown that the m-RBF has a faster learning rate and can avoid local minima; the control scheme based on m-RBF has a excellent performance on control accuracy, robustness and self-adaption, which is especially appropriate for real-time control, like tea picking robot.https://doi.org/10.1038/s41598-025-10981-4Dead zone nonlinearityRBF neural networkManipulatorTrackingControlTea harvesting |
| spellingShingle | Yu Han Zhiyu Song Wenyu Yi Caixue Zhan Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF Scientific Reports Dead zone nonlinearity RBF neural network Manipulator Tracking Control Tea harvesting |
| title | Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF |
| title_full | Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF |
| title_fullStr | Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF |
| title_full_unstemmed | Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF |
| title_short | Adaptive robust control of tea-picking-manipulator’s position tracking based on dead zone compensation with modified RBF |
| title_sort | adaptive robust control of tea picking manipulator s position tracking based on dead zone compensation with modified rbf |
| topic | Dead zone nonlinearity RBF neural network Manipulator Tracking Control Tea harvesting |
| url | https://doi.org/10.1038/s41598-025-10981-4 |
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