Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot
Adaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10379446/ |
| _version_ | 1797354568637480960 |
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| author | Jie Zhou Huanfeng Peng Manxu Zheng Zhe Wei Tao Fan Rong Song |
| author_facet | Jie Zhou Huanfeng Peng Manxu Zheng Zhe Wei Tao Fan Rong Song |
| author_sort | Jie Zhou |
| collection | DOAJ |
| description | Adaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, achieving smooth switching of human-dominant mode, robot-dominant mode, and soft-stop mode. Besides, a linear active disturbance rejection controller is designed as the low-level position controller. Four healthy participants and two stroke survivors are recruited to conduct robot-assisted walking experiments using the TD-MACCS. The results show that the TD-MACCS can smoothly switch three control modes while guaranteeing trajectory tracking accuracy. Moreover, we find that appropriately increasing the upper bound of the deformation factor can enhance the average walking speed (AWS) and root mean square of trajectory deviation (RMSTD). |
| first_indexed | 2024-03-08T13:52:19Z |
| format | Article |
| id | doaj.art-79cd4aa6293547fb986bd65d9e4bfd4a |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-08T13:52:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-79cd4aa6293547fb986bd65d9e4bfd4a2024-01-16T00:00:30ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102024-01-013231432410.1109/TNSRE.2023.334833210379446Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation RobotJie Zhou0https://orcid.org/0000-0003-2335-3573Huanfeng Peng1Manxu Zheng2https://orcid.org/0000-0003-2906-2857Zhe Wei3https://orcid.org/0000-0002-6059-7326Tao Fan4https://orcid.org/0000-0002-5694-8752Rong Song5https://orcid.org/0000-0003-3662-116XSchool of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaDepartment of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, ChinaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang, ChinaDepartment of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, ChinaSchool of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaAdaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, achieving smooth switching of human-dominant mode, robot-dominant mode, and soft-stop mode. Besides, a linear active disturbance rejection controller is designed as the low-level position controller. Four healthy participants and two stroke survivors are recruited to conduct robot-assisted walking experiments using the TD-MACCS. The results show that the TD-MACCS can smoothly switch three control modes while guaranteeing trajectory tracking accuracy. Moreover, we find that appropriately increasing the upper bound of the deformation factor can enhance the average walking speed (AWS) and root mean square of trajectory deviation (RMSTD).https://ieeexplore.ieee.org/document/10379446/Wearable lower limb rehabilitation robotphysical human-robot interactiontrajectory deformation algorithmdynamic motion primitiveslinear active disturbance rejection control |
| spellingShingle | Jie Zhou Huanfeng Peng Manxu Zheng Zhe Wei Tao Fan Rong Song Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot IEEE Transactions on Neural Systems and Rehabilitation Engineering Wearable lower limb rehabilitation robot physical human-robot interaction trajectory deformation algorithm dynamic motion primitives linear active disturbance rejection control |
| title | Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot |
| title_full | Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot |
| title_fullStr | Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot |
| title_full_unstemmed | Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot |
| title_short | Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot |
| title_sort | trajectory deformation based multi modal adaptive compliance control for a wearable lower limb rehabilitation robot |
| topic | Wearable lower limb rehabilitation robot physical human-robot interaction trajectory deformation algorithm dynamic motion primitives linear active disturbance rejection control |
| url | https://ieeexplore.ieee.org/document/10379446/ |
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