Journal of Intelligent & Robotic Systems

, Volume 80, Issue 1, pp 15–31 | Cite as

Neural Network Control of a Rehabilitation Robot by State and Output Feedback

  • Wei HeEmail author
  • Shuzhi Sam Ge
  • Yanan Li
  • Effie Chew
  • Yee Sien Ng


In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.


Adaptive neural network control Full state feedback control Lyapunov’s direct method Output feedback control Rehabilitation robot 


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Wei He
    • 1
    Email author
  • Shuzhi Sam Ge
    • 2
  • Yanan Li
    • 3
  • Effie Chew
    • 4
  • Yee Sien Ng
    • 5
  1. 1.Center for Robotics and School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of Electrical & Computer EngineeringNational University of SingaporeDowntown CoreSingapore
  3. 3.Institute for Infocomm Research, Agency for Science, Technology and ResearchDowntown CoreSingapore
  4. 4.Division of NeurologyNational University HospitalDowntown CoreSingapore
  5. 5.Department of Rehabilitation MedicineSingapore General HospitalDowntown CoreSingapore

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