RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties
In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for uppe...
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doaj-df6278f88362404ba3e487f7dd23b9942021-04-05T17:30:02ZengIEEEIEEE Access2169-35362019-01-01713463513464610.1109/ACCESS.2019.29419738840862RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic UncertaintiesQingcong Wu0https://orcid.org/0000-0002-7774-6092Bai Chen1Hongtao Wu2College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaIn recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training.https://ieeexplore.ieee.org/document/8840862/Upper-limb exoskeletonrehabilitation trainingadaptive backstepping sliding mode controlneural uncertainty observerLyapunov stability theory |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingcong Wu Bai Chen Hongtao Wu |
spellingShingle |
Qingcong Wu Bai Chen Hongtao Wu RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties IEEE Access Upper-limb exoskeleton rehabilitation training adaptive backstepping sliding mode control neural uncertainty observer Lyapunov stability theory |
author_facet |
Qingcong Wu Bai Chen Hongtao Wu |
author_sort |
Qingcong Wu |
title |
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties |
title_short |
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties |
title_full |
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties |
title_fullStr |
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties |
title_full_unstemmed |
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties |
title_sort |
rbfn-based adaptive backstepping sliding mode control of an upper-limb exoskeleton with dynamic uncertainties |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training. |
topic |
Upper-limb exoskeleton rehabilitation training adaptive backstepping sliding mode control neural uncertainty observer Lyapunov stability theory |
url |
https://ieeexplore.ieee.org/document/8840862/ |
work_keys_str_mv |
AT qingcongwu rbfnbasedadaptivebacksteppingslidingmodecontrolofanupperlimbexoskeletonwithdynamicuncertainties AT baichen rbfnbasedadaptivebacksteppingslidingmodecontrolofanupperlimbexoskeletonwithdynamicuncertainties AT hongtaowu rbfnbasedadaptivebacksteppingslidingmodecontrolofanupperlimbexoskeletonwithdynamicuncertainties |
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1721539482194280448 |