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Minimizing human-exoskeleton interaction force by using global fast sliding mode control

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  • Robot and Applications
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Abstract

A critical issue in the model-based control of performance-augmenting exoskeleton systems is the unknown nonlinear dynamic properties of the systems or the uncertainties. An improper estimation of the system dynamics can cause instabilities in the system and generate considerable human-exoskeleton interaction forces during human motions. Thus, the controller of such exoskeleton systems needs to add robustness to stabilize it against the uncertainties. In this paper, we propose a global fast sliding mode control algorithm integrated in a hybrid controller for each exoskeleton leg to minimize human-exoskeleton interaction forces. By doing so, the proposed algorithm does not require an exact estimation of the dynamic properties of the exoskeleton system, but still minimizes the physical human-exoskeleton interaction (pHEI) forces. Finally, the performance of the proposed algorithm is verified by experiments on our lower exoskeleton system, which is used for human power augmentation and called “PRMI” exoskeleton. Our experimental results show that the proposed control algorithm provides a good control quality for the PRMI exoskeleton. The PRMI exoskeleton can support a wearer carrying heavy load while tracking the rapid movements of the wearer without obstructing them.

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology of China and the Faculty of Electronics, Industrial University of Ho Chi Minh City, Hanoi, Vietnam

    Duong Mien Ka & Tran Huu Toan

  2. School of Automation Engineering, University of Electronic Science and Technology of China, Beijing, China

    Cheng Hong & Jing Qiu

Authors
  1. Duong Mien Ka
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  2. Cheng Hong
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  3. Tran Huu Toan
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  4. Jing Qiu
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Corresponding author

Correspondence to Duong Mien Ka.

Additional information

Recommended by Associate Editor Sukho Park under the direction of Editor PooGyeon Park. This research was supported by the grant of National Natural Science Foundation of China (Grant No. 61273256). The authors would like to thank the associate editor and the reviewers for their valuable comments.

Duong Mien Ka is a Ph.D. research student at University of Electronic Science and Technology of China. Now, he is also a lecturer at Faculty of Electronic Technology, Industrial University of Ho Chi Minh City, Viet Nam. He received the M.S. degree in Automation from Ho Chi Minh City University of Technology in 2010. His current research interests include mechatronics systems, intelligent robotic control, and human power augmentation exoskeleton.

Cheng Hong is a full Professor in School of Automation, a vice director of Center for Robotics, UESTC. He received Ph.D. degree in Pattern Recognition and Intelligent Systems from Xi’an Jiaotong University in 2003. Now he is the founding director of Pattern Recognition and Machine Intelligence Lab, UESTC. Before this, He was a postdoctoral at Computer Science School, Carnegie Mellon University, USA from 2006 to 2009. He was an associate Professor of Xi’an Jiaotong University since 2005. Since July 2000, he had been with Xi’an Jiaotong University, where he had been a team leader of intelligent Vehicle Group at the Institute of Artificial Intelligence and Robotics before going to USA. His current research interests include computer vision and machine learning, robotics, human computer interaction, multimedia signal processing. The team that Dr. Cheng was leading in XJTU had developed an intelligent driving platform-Spring robot, which has important social effect in China. Dr. Cheng has over 50 academic publications including two books-“Digital Signal Processing (Tsinghua University Press, Sep. 2007)” and “Autonomous Intelligent Vehicles: Theory, Algorithms and Implementation (Springer, Dec. 2011)”. He has been a senior member of IEEE, ACM, and Associate Editor of IEEE Computational Intelligence Magazine. He is a reviewer for many important journals and conferences (IEEE TITS, MAV, CVPR, ICCV, ITSC, IVS, ACCV, etc.). Dr. Cheng serves as Finance Chair of ICME 2014, Local arrangement chair of VLPR 2012, Registration Chair of the 2005 IEEE ICVES Dr. Cheng was teaching Digital Signal Processing and Introduction to Embedded systems for junior students at Automation department and also Advanced Digital Signal Processing for graduate students in Xi’an Jiaotong University. Now he is teaching Pattern Recognition and Machine Learning and Computer Vision for graduate students, and also Introduction to Artificial Intelligence and Digital Image Processing for junior students in UESTC.

Tran Huu Toan received the M.S. degree in Automation from Ho Chi Minh City University of Technology in 2009, and the Ph.D degree in Electronic Science and Technology from University of Electronic Science and Technology of China in 2015. His research interests include control applications, robot learning, and human-robot interaction.

Jing Qiu is currently working in the School of Mechatronics Engineering at the University of Electronic Science and Technology of China (UESTC). Prior to joining UESTC, she was a research assistant at the Institute of Ergonomics at Darmstadt University of Technology. She received her Ph.D. in ergonomics from Darmstadt University of Technology in 2010.

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Ka, D.M., Hong, C., Toan, T.H. et al. Minimizing human-exoskeleton interaction force by using global fast sliding mode control. Int. J. Control Autom. Syst. 14, 1064–1073 (2016). https://doi.org/10.1007/s12555-014-0395-7

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  • DOI: https://doi.org/10.1007/s12555-014-0395-7

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