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Research on optimization of human-machine interaction control strategy for exoskeleton based on impedance control

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Abstract

This work proposes an optimized human-machine interaction control strategy for exoskeleton based on traditional impedance control. The traditional impedance control has poor robustness due to the lack of accurate understanding of the environment, so it cannot accurately track the desired interactive force. Firstly, an adaptive controller is designed based on a model reference adaptive control algorithm to convert the force error into a position correction, which reduces the interaction force error. Then a PID-like algorithm is introduced into the system to improve the impedance relationship. Finally, the whole control system is verified by simulation experiments. It could be found that the interactive force error is reduced with the addition of an adaptive controller, but the system response is slowed down. At last, the PID algorithm is introduced into the control system. As a result, the system response speed and interaction force error are both further improved.

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Acknowledgments

This work is supported by the Natural Science Foundation of Liaoning Province Medical and Industrial Cross Joint Fund Project (No. 2022-YGJC-07) China, Youth Science Foundation of the National Natural Science Foundation of China (No. 61803272), National Key Laboratory Foundation 2021-JCJQ-LB-006 (No. 6142411342110), China; State Grid Liaoning Electric Power Supply Co., Ltd., Technology Project (No. 2022YF-95) China.

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Authors and Affiliations

  1. Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China

    Guanchao Li, Hualong Xie & Xiangxiang Wang

  2. Liaoning Power Transmission and Transformation Engineering Co., Ltd., Shenyang, 110819, China

    Zhen Chen

Authors
  1. Guanchao Li
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  2. Hualong Xie
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  3. Xiangxiang Wang
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  4. Zhen Chen
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Corresponding author

Correspondence to Hualong Xie.

Additional information

Guanchao Li is a Postgraduate of the School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China. He received the B.S. degree in Mechanical Engineering from Qingdao University, China. His research interests include signal processing, pattern recognition and lower extremity exoskeleton.

Hualong Xie received his B.S. degree in Mechanical Electronic Engineering, M.S. degree in Mechanical Design and Theory, and a Ph.D. degree in Control Theory and Control Engineering from Northeastern University, China, in 2000, 2003, and 2006, respectively. Since 2010, he has been an Associate Professor at Northeastern University. His research interests include robotics, intelligent control, intelligent bionic leg, and biomechanics.

Xiangxiang Wang received his B.S. degree and M.S. degree in Mechanical Electronic Engineering from Shandong University of Technology, Shandong China, in 2017 and 2020, respectively. He is currently a Ph.D. student at the Department of Mechanical Engineering and Automation, Northeastern University. His research area is underwater bionic robot technology.

Zhen Chen received his B.S. degree in Mechanical Manufacturing Process and Equipment from Shenyang University of Technology in 1999, M.S. in Mechanical and Electronic Engineering from Shenyang University of Technology in 2006, and Ph.D. degree in Pattern Recognition and Intelligent System from Northeastern University in 2013. He has been a Senior Engineer of State Grid Liaoning Power Transmission and Transformation Engineering Company and a leading professional talent of State Grid Corporation. His research interests include intelligent equipment for power grid construction, robotics, pattern recognition and control.

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Li, G., Xie, H., Wang, X. et al. Research on optimization of human-machine interaction control strategy for exoskeleton based on impedance control. J Mech Sci Technol 37, 1411–1420 (2023). https://doi.org/10.1007/s12206-023-0227-x

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  • DOI: https://doi.org/10.1007/s12206-023-0227-x

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