Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent
During the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator’s end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position contr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/49 |
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| author | Linshen Li Fan Wang Huilin Tang Yanbing Liang |
| author_facet | Linshen Li Fan Wang Huilin Tang Yanbing Liang |
| author_sort | Linshen Li |
| collection | DOAJ |
| description | During the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator’s end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. Firstly, a sliding mode controller is designed for position control to mitigate uncertainties, including friction and unknown perturbations within the manipulator system. Secondly, the RBFNN, known for its nonlinear fitting capabilities, is employed to identify the system throughout the iterative process. Lastly, a gradient descent method adjusts the impedance parameters iteratively. Through simulation and experimentation, the efficacy of the proposed method in achieving precise force and position control is confirmed. Compared to traditional impedance control, manual adjustment of impedance parameters is unnecessary, and the method can adapt to tasks involving objects of varying stiffness, highlighting its superiority. |
| format | Article |
| id | doaj-art-5732e46fb5c74ab0bdade3a5f01b6cf7 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5732e46fb5c74ab0bdade3a5f01b6cf72025-01-10T13:20:41ZengMDPI AGSensors1424-82202024-12-012514910.3390/s25010049Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient DescentLinshen Li0Fan Wang1Huilin Tang2Yanbing Liang3Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, ChinaDuring the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator’s end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. Firstly, a sliding mode controller is designed for position control to mitigate uncertainties, including friction and unknown perturbations within the manipulator system. Secondly, the RBFNN, known for its nonlinear fitting capabilities, is employed to identify the system throughout the iterative process. Lastly, a gradient descent method adjusts the impedance parameters iteratively. Through simulation and experimentation, the efficacy of the proposed method in achieving precise force and position control is confirmed. Compared to traditional impedance control, manual adjustment of impedance parameters is unnecessary, and the method can adapt to tasks involving objects of varying stiffness, highlighting its superiority.https://www.mdpi.com/1424-8220/25/1/49manipulatorimpedance controlgradient descentRBFNN |
| spellingShingle | Linshen Li Fan Wang Huilin Tang Yanbing Liang Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent Sensors manipulator impedance control gradient descent RBFNN |
| title | Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent |
| title_full | Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent |
| title_fullStr | Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent |
| title_full_unstemmed | Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent |
| title_short | Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent |
| title_sort | variable parameter impedance control of manipulator based on rbfnn and gradient descent |
| topic | manipulator impedance control gradient descent RBFNN |
| url | https://www.mdpi.com/1424-8220/25/1/49 |
| work_keys_str_mv | AT linshenli variableparameterimpedancecontrolofmanipulatorbasedonrbfnnandgradientdescent AT fanwang variableparameterimpedancecontrolofmanipulatorbasedonrbfnnandgradientdescent AT huilintang variableparameterimpedancecontrolofmanipulatorbasedonrbfnnandgradientdescent AT yanbingliang variableparameterimpedancecontrolofmanipulatorbasedonrbfnnandgradientdescent |