Adaptive Neural Control for Safe Human-Robot Interaction
This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the in...
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| Format: | Thesis |
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Curtin University
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/68285 |
| _version_ | 1848761371658813440 |
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| author | Rahimi Nohooji, Hamed |
| author_facet | Rahimi Nohooji, Hamed |
| author_sort | Rahimi Nohooji, Hamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation. |
| first_indexed | 2025-11-14T10:30:37Z |
| format | Thesis |
| id | curtin-20.500.11937-68285 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:30:37Z |
| publishDate | 2017 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-682852019-04-29T03:54:31Z Adaptive Neural Control for Safe Human-Robot Interaction Rahimi Nohooji, Hamed This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation. 2017 Thesis http://hdl.handle.net/20.500.11937/68285 Curtin University fulltext |
| spellingShingle | Rahimi Nohooji, Hamed Adaptive Neural Control for Safe Human-Robot Interaction |
| title | Adaptive Neural Control for Safe Human-Robot Interaction |
| title_full | Adaptive Neural Control for Safe Human-Robot Interaction |
| title_fullStr | Adaptive Neural Control for Safe Human-Robot Interaction |
| title_full_unstemmed | Adaptive Neural Control for Safe Human-Robot Interaction |
| title_short | Adaptive Neural Control for Safe Human-Robot Interaction |
| title_sort | adaptive neural control for safe human-robot interaction |
| url | http://hdl.handle.net/20.500.11937/68285 |