Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot
In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation train...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9174808/ |
| _version_ | 1818662499591913472 |
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| author | Peng Zhang Junxia Zhang Zunhao Zhang |
| author_facet | Peng Zhang Junxia Zhang Zunhao Zhang |
| author_sort | Peng Zhang |
| collection | DOAJ |
| description | In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by co-simulation and human experiment in accordance to track following performance and disturbances rejection ability. |
| first_indexed | 2024-12-17T05:01:56Z |
| format | Article |
| id | doaj.art-720c75322b724b1689d7b1fb05936de9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:01:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-720c75322b724b1689d7b1fb05936de92022-12-21T22:02:32ZengIEEEIEEE Access2169-35362020-01-01815553815554710.1109/ACCESS.2020.30187379174808Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation RobotPeng Zhang0https://orcid.org/0000-0002-7786-8137Junxia Zhang1https://orcid.org/0000-0002-8753-1650Zunhao Zhang2Tianjin Key Laboratory for Integrated Design, Tianjin University of Science and Technology, Tianjin, ChinaTianjin Key Laboratory for Integrated Design, Tianjin University of Science and Technology, Tianjin, ChinaTianjin Key Laboratory for Integrated Design, Tianjin University of Science and Technology, Tianjin, ChinaIn accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by co-simulation and human experiment in accordance to track following performance and disturbances rejection ability.https://ieeexplore.ieee.org/document/9174808/Rehabilitation robotradial basis functions neural networkvariable sliding mode controlcoordinated movement |
| spellingShingle | Peng Zhang Junxia Zhang Zunhao Zhang Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot IEEE Access Rehabilitation robot radial basis functions neural network variable sliding mode control coordinated movement |
| title | Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot |
| title_full | Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot |
| title_fullStr | Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot |
| title_full_unstemmed | Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot |
| title_short | Design of RBFNN-Based Adaptive Sliding Mode Control Strategy for Active Rehabilitation Robot |
| title_sort | design of rbfnn based adaptive sliding mode control strategy for active rehabilitation robot |
| topic | Rehabilitation robot radial basis functions neural network variable sliding mode control coordinated movement |
| url | https://ieeexplore.ieee.org/document/9174808/ |
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