Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

  • Mien Ka Duong
  • Hong ChengEmail author
  • Huu Toan Tran
  • Qiu Jing


A critical issue in the control of exoskeleton systems is unknown nonlinear dynamic properties of the system. The improper estimation of those unknown properties can cause considerable human-exoskeleton interaction force during human’s movements. It is really challenging to exactly estimate the parameters of dynamic models. In this paper, we propose a novel exoskeleton control algorithm to both compensate for the dynamic uncertainty error and minimize the human-exoskeleton interaction force. We have built a virtual torque controller based on dynamic models of a lower exoskeleton and have used an approximation of a Radial Basis Function (RBF) neural network to compensate for the dynamic uncertainty error. By doing so, we avoid using complicated force sensors installed on the human-exoskeleton interface and minimize the physical Human-Robot Interaction (pHRI) force. Moreover, we introduce the prototype of our exoskeleton system, called ‘PRMI’ exoskeleton system. Finally, we validated the proposed algorithm on this system, and the experimental results show that the proposed control algorithm provides a good control quality for the ‘PRMI’ exoskeleton system by compensating for dynamic uncertainty error.


Exoskeleton RBF networks Virtual torque control Human-exoskeleton interaction 


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Mien Ka Duong
    • 1
    • 2
  • Hong Cheng
    • 1
    Email author
  • Huu Toan Tran
    • 1
  • Qiu Jing
    • 1
  1. 1.Center for Robotics, School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaSichuanPeople’s Republic of China
  2. 2.Faculty of Electronic TechnologyIndustrial University of Ho Chi Minh CityHo Chi Minh CityVietnam

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