Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network
This study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6053 |
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| author | Jing Li Huimin Jiang Moyao Gao Shuang Li Zhanli Wang Zaixiang Pang Yang Zhang Yang Jiao |
| author_facet | Jing Li Huimin Jiang Moyao Gao Shuang Li Zhanli Wang Zaixiang Pang Yang Zhang Yang Jiao |
| author_sort | Jing Li |
| collection | DOAJ |
| description | This study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. The RBF neural network effectively approximates uncertainties arising from lower-limb muscle perturbations, while adaptive regulation through parameter simplification ensures precise torque tracking at each joint, meeting real-time rehabilitation requirements. MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. Experimental results show maximum angular errors of 1.77° (hip flexion/extension), 1.87° (knee flexion/extension), and 0.72° (ankle dorsiflexion/plantarflexion). The integrated motion capture system enables the development of patient-specific skeletal muscle models and optimized gait trajectories, ensuring both training efficacy and safety for spasticity patients. |
| format | Article |
| id | doaj-art-4ccfc6041f9148d18c6dfb5a11d153c4 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4ccfc6041f9148d18c6dfb5a11d153c42025-08-20T02:33:01ZengMDPI AGApplied Sciences2076-34172025-05-011511605310.3390/app15116053Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural NetworkJing Li0Huimin Jiang1Moyao Gao2Shuang Li3Zhanli Wang4Zaixiang Pang5Yang Zhang6Yang Jiao7School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, ChinaInstitute of Electromechanical Technology, Jilin Academy of Agricultural Machinery, Changchun 130021, ChinaSchool of Mechatronic Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, ChinaAeronautical Basic College, Air Force Aviation University, Changchun 130022, ChinaThis study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. The RBF neural network effectively approximates uncertainties arising from lower-limb muscle perturbations, while adaptive regulation through parameter simplification ensures precise torque tracking at each joint, meeting real-time rehabilitation requirements. MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. Experimental results show maximum angular errors of 1.77° (hip flexion/extension), 1.87° (knee flexion/extension), and 0.72° (ankle dorsiflexion/plantarflexion). The integrated motion capture system enables the development of patient-specific skeletal muscle models and optimized gait trajectories, ensuring both training efficacy and safety for spasticity patients.https://www.mdpi.com/2076-3417/15/11/6053lower limb rehabilitation robotRBF neural networkiterative learningtrajectory trackingrehabilitation training experiment |
| spellingShingle | Jing Li Huimin Jiang Moyao Gao Shuang Li Zhanli Wang Zaixiang Pang Yang Zhang Yang Jiao Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network Applied Sciences lower limb rehabilitation robot RBF neural network iterative learning trajectory tracking rehabilitation training experiment |
| title | Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network |
| title_full | Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network |
| title_fullStr | Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network |
| title_full_unstemmed | Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network |
| title_short | Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network |
| title_sort | research on iterative learning method for lower limb exoskeleton rehabilitation robot based on rbf neural network |
| topic | lower limb rehabilitation robot RBF neural network iterative learning trajectory tracking rehabilitation training experiment |
| url | https://www.mdpi.com/2076-3417/15/11/6053 |
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