Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network
In the rehabilitation training process of lower limb rehabilitation robots, the existence of uncertain factors such as model parameters and environmental interference will affect the accuracy of trajectory tracking of the robot. To solve this problem, an adaptive compensation control based on the ra...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2024-04-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2024.04.008 |
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| author | Li Dongqi Qin Jianjun Sun Maolin Zheng Haoran Li Wei |
| author_facet | Li Dongqi Qin Jianjun Sun Maolin Zheng Haoran Li Wei |
| author_sort | Li Dongqi |
| collection | DOAJ |
| description | In the rehabilitation training process of lower limb rehabilitation robots, the existence of uncertain factors such as model parameters and environmental interference will affect the accuracy of trajectory tracking of the robot. To solve this problem, an adaptive compensation control based on the radial basis function (RBF) neural network is proposed. This control method can improve the accuracy of mechanical system trajectory tracking. Firstly, a closed chain horizontal lower limb rehabilitation robot structure with four working modes and stable movement is designed. Secondly, the Lagrange method is used to solve the kinetic nominal model, the uncertainty factors such as model parameters and external interference of the rehabilitation device are separated, and the adaptive compensation algorithm based on the RBF neural network is designed for the approximate control. Finally, the Matlab/Simulink environment is used to verify the effectiveness of the control strategy. The results show that, compared with the traditional fuzzy proportional integral derivative (PID) control method, the adaptive compensation algorithm based on the RBF neural network has a faster response speed and better tracking effect in human gait curve trajectory tracking. Moreover, the peak angle errors of the hip joint and the knee joint trajectory tracking are 0.08° and 0.13° respectively, which are much less than the rotation angle of patients' lower limbs in rehabilitation exercise. A single-leg prototype experiment is designed to show that the RBF compensation adaptive controller used in the study can achieve high precision tracking results and meet the safety requirements of patients in rehabilitation training. |
| format | Article |
| id | doaj-art-10dbe0e08b4844c580c1d50a8d02f3e6 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-10dbe0e08b4844c580c1d50a8d02f3e62025-01-10T15:00:17ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392024-04-0148606855347358Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural NetworkLi DongqiQin JianjunSun MaolinZheng HaoranLi WeiIn the rehabilitation training process of lower limb rehabilitation robots, the existence of uncertain factors such as model parameters and environmental interference will affect the accuracy of trajectory tracking of the robot. To solve this problem, an adaptive compensation control based on the radial basis function (RBF) neural network is proposed. This control method can improve the accuracy of mechanical system trajectory tracking. Firstly, a closed chain horizontal lower limb rehabilitation robot structure with four working modes and stable movement is designed. Secondly, the Lagrange method is used to solve the kinetic nominal model, the uncertainty factors such as model parameters and external interference of the rehabilitation device are separated, and the adaptive compensation algorithm based on the RBF neural network is designed for the approximate control. Finally, the Matlab/Simulink environment is used to verify the effectiveness of the control strategy. The results show that, compared with the traditional fuzzy proportional integral derivative (PID) control method, the adaptive compensation algorithm based on the RBF neural network has a faster response speed and better tracking effect in human gait curve trajectory tracking. Moreover, the peak angle errors of the hip joint and the knee joint trajectory tracking are 0.08° and 0.13° respectively, which are much less than the rotation angle of patients' lower limbs in rehabilitation exercise. A single-leg prototype experiment is designed to show that the RBF compensation adaptive controller used in the study can achieve high precision tracking results and meet the safety requirements of patients in rehabilitation training.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2024.04.008Lower limb rehabilitation robotClosed chain structureRBF neural networkUncertainty Adaptive compensation control |
| spellingShingle | Li Dongqi Qin Jianjun Sun Maolin Zheng Haoran Li Wei Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network Jixie chuandong Lower limb rehabilitation robot Closed chain structure RBF neural network Uncertainty Adaptive compensation control |
| title | Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network |
| title_full | Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network |
| title_fullStr | Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network |
| title_full_unstemmed | Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network |
| title_short | Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network |
| title_sort | adaptive compensation control of closed chain lower limb rehabilitation robots based on the rbf neural network |
| topic | Lower limb rehabilitation robot Closed chain structure RBF neural network Uncertainty Adaptive compensation control |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2024.04.008 |
| work_keys_str_mv | AT lidongqi adaptivecompensationcontrolofclosedchainlowerlimbrehabilitationrobotsbasedontherbfneuralnetwork AT qinjianjun adaptivecompensationcontrolofclosedchainlowerlimbrehabilitationrobotsbasedontherbfneuralnetwork AT sunmaolin adaptivecompensationcontrolofclosedchainlowerlimbrehabilitationrobotsbasedontherbfneuralnetwork AT zhenghaoran adaptivecompensationcontrolofclosedchainlowerlimbrehabilitationrobotsbasedontherbfneuralnetwork AT liwei adaptivecompensationcontrolofclosedchainlowerlimbrehabilitationrobotsbasedontherbfneuralnetwork |