Neural adaptive assist-as-needed control for rehabilitation robots
© 2018 Australasian Robotics and Automation Association. All rights reserved. Robot-assisted therapy can improve motor function in patients recovering from stroke. Assist-as-needed algorithms provide only minimal robotic assistance in the therapy, thus requiring significant effort from the impaired...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/70043 |
| _version_ | 1848762200354717696 |
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| author | Rahimi, H. Howard, Ian Cui, Lei |
| author_facet | Rahimi, H. Howard, Ian Cui, Lei |
| author_sort | Rahimi, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 Australasian Robotics and Automation Association. All rights reserved. Robot-assisted therapy can improve motor function in patients recovering from stroke. Assist-as-needed algorithms provide only minimal robotic assistance in the therapy, thus requiring significant effort from the impaired subject. This paper presents an adaptive neural assist-as-needed controller for rehabilitative robots. The controller combines the Lyapunov direct method with the computed torque control and neural networks. Robot assistance is limited to only as needed by adding the force reducing term into the adaptive control law. This paper shows that by the presented method the tracking error converges to a small value around zero while the neural network weights and system uncertainties remain bounded. Simulation on a robot manipulator model is presented to demonstrate the effectiveness of the proposed meth. |
| first_indexed | 2025-11-14T10:43:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-70043 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:43:47Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-700432020-07-27T03:04:01Z Neural adaptive assist-as-needed control for rehabilitation robots Rahimi, H. Howard, Ian Cui, Lei © 2018 Australasian Robotics and Automation Association. All rights reserved. Robot-assisted therapy can improve motor function in patients recovering from stroke. Assist-as-needed algorithms provide only minimal robotic assistance in the therapy, thus requiring significant effort from the impaired subject. This paper presents an adaptive neural assist-as-needed controller for rehabilitative robots. The controller combines the Lyapunov direct method with the computed torque control and neural networks. Robot assistance is limited to only as needed by adding the force reducing term into the adaptive control law. This paper shows that by the presented method the tracking error converges to a small value around zero while the neural network weights and system uncertainties remain bounded. Simulation on a robot manipulator model is presented to demonstrate the effectiveness of the proposed meth. 2016 Conference Paper http://hdl.handle.net/20.500.11937/70043 restricted |
| spellingShingle | Rahimi, H. Howard, Ian Cui, Lei Neural adaptive assist-as-needed control for rehabilitation robots |
| title | Neural adaptive assist-as-needed control for rehabilitation robots |
| title_full | Neural adaptive assist-as-needed control for rehabilitation robots |
| title_fullStr | Neural adaptive assist-as-needed control for rehabilitation robots |
| title_full_unstemmed | Neural adaptive assist-as-needed control for rehabilitation robots |
| title_short | Neural adaptive assist-as-needed control for rehabilitation robots |
| title_sort | neural adaptive assist-as-needed control for rehabilitation robots |
| url | http://hdl.handle.net/20.500.11937/70043 |