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Adaptive control with a fuzzy tuner for cable-based rehabilitation robot

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Abstract

Since there are external uncertainties in the environment and the dynamic properties of human arm are time-varying during movement, it is difficult to achieve good tracking performance during robot-aided rehabilitation. The purpose of this study is to develop an adaptive controller with a fuzzy tuner for a cable-based rehabilitation robot. The fuzzy tuner can adjust the control parameters according to position error and the change of error, thus the time-varying control parameters can be optimized by this tuner. To verify the proposed controller, simulation and experiment studies using the cable-based rehabilitation robot are carried out. The rehabilitation robot is employed to complete two facilitation movements in the experiment. Results show that the adaptive controller can attain better control performance after tuning the control parameters.

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Author information

Authors and Affiliations

  1. Key Laboratory of Sensing Technology and Biomedical Instrument of GuangDong province, School of Engineering, Sun Yat-sen University, Guangzhou, Guang Dong, P. R. China

    Jin Yang, Di Ao & Rong Song

  2. College of Automation, South China University of Technology, Guangzhou, Guang Dong, P. R. China

    Hang Su & Zhijun Li

Authors
  1. Jin Yang
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  2. Hang Su
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  4. Di Ao
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  5. Rong Song
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Corresponding author

Correspondence to Rong Song.

Additional information

Recommended by Associate Editor Sung Jin Yoo under the direction of Editor Fuchun Sun. The project is supported by the National Natural Science Foundation of China (Grant No. 61273359) and the Guangzhou Research Collaborative Innovation Projects (No. 1561000248).

Jin Yang received the B.Eng. degree from Sun Yat-Sen University, P. R. China, in 2012. He is now with Department of Biomedical Engineering, Sun Yat-Sen University, P. R. China, pursuiting his master degree. His interests include design of cable-based rehabilitation robot, fuzzy control, impedance control and EMGbased control.

Hang Su received the B.Eng. degree from Yantai University, China, in 2012. He is now with Department of Automation Science and Engineering, South China University of Technology, P. R. China, pursuiting his master degree. His interests include control of the exoskeleton rehabilitation robot, adaptive control, and nonlinear systems; biological signal processing and recognition, EMG signals.

Zhijun Li received the Dr. Eng. degree in mechatronics, Shanghai Jiao Tong University, P. R. China, in 2002. From 2003 to 2005, he was a postdoctoral fellow in Department of Mechanical Engineering and Intelligent systems, The University of Electro-Communications, Tokyo, Japan. From 2005 to 2006, he was a research fellow in the Department of Electrical and Computer Engineering, National University of Singapore. From 2007-2011, he was an Associate Professor in the Department of Automation, Shanghai Jiao Tong University, P. R. China. Currently, he is a Professor in College of Automation Science and Engineering, South China university of Technology, Guangzhou, China. He is serving as an Editor-at-large of Journal of Intelligent & Robotic System. Dr. Li’s current research interests include adaptive/robust control, mobile manipulator, teleoperation system, etc.

Di Ao received the B.Eng. degree from Sun Yat-Sen University, P. R. China, in 2012. She is now with Department of Biomedical Engineering, Sun Yat-Sen University, P. R. China, pursuiting her master degree. Her interests include design of ankle rehabilitation robot and musculoskeletal modeling.

Rong Song received the B.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 1999, the M.S. degree in electronic engineering from Shantou University, Shantou, China, in 2002, and the Ph.D. degree in biomedical engineering from the Hong Kong Polytechnic University, Kowloon, Hong Kong, in 2006. He is currently an associate professor in School of Engineering, Sun Yat-sen University, P. R. China. His research interests include musculoskeletal modeling, biomedical signal processing, human motion analysis, and robot-assisted stroke rehabilitation.

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Yang, J., Su, H., Li, Z. et al. Adaptive control with a fuzzy tuner for cable-based rehabilitation robot. Int. J. Control Autom. Syst. 14, 865–875 (2016). https://doi.org/10.1007/s12555-015-0049-4

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