Impedance control of a rehabilitation robot for interactive training

Abstract

In this paper, neural networks based impedance control is developed for a wearable rehabilitation robot in interactions with humans and the environments. The dynamics of the robot are represented by an n-link rigid robotic manipulator. To deal with the system uncertainties and improve the robustness of the system, the adaptive neural networks are used to approximate the unknown model of the constrained robot. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved based on the Lyapunov method. The states of the system converge to a small neighborhood of zero by properly choosing control gains. Extensive simulations are conducted to verify the proposed control. © 2012 Springer-Verlag.