Shao, F, Meng, W, Ai, Q et al. (1 more author) (2021) Neural Network Adaptive Control of Hand Rehabilitation Robot Driven by Flexible Pneumatic Muscles. In: 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE). 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE), 03-05 Feb 2021, Budapest, Hungary. IEEE , pp. 59-63. ISBN 978-1-6654-3053-1
Abstract
The aim of this study is to design a reliable and stable controller for hand rehabilitation robot driven by flexible pneumatic muscles(FPMs) for post stroke patients. Position control is key to perform effective rehabilitation robotic exercise. However, it is difficult to achieve precise control due to the nonlinearity and hysteresis of the flexible muscles. The efficient control system is required to realize the high-precision control of the joint angle. In this paper, to achieve the stability and anti-interference ability of the system, an improved neural network adaptive control(INNAC) method is proposed. The neural network is used to estimate the unknown items and the adaptive control is used to realize the adaptive characteristics in the unknown environment, so as to realize the stability and high precision control of the control system when encountering human interferences. Finally, experiments were carried out on robot with human participants for five fingers movement assistance. The results show that the control system can achieve good control effect and anti-interference ability.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Training; Trajectory tracking; Neural networks; Rehabilitation robotics; Pneumatic systems; Muscles; Adaptive control |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2021 14:49 |
Last Modified: | 05 Aug 2021 14:49 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icmre51691.2021.9384827 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176185 |