scholarly journals Passive Friction Compensation Using a Nonlinear Disturbance Observer for Flexible Joint Robots with Joint Torque Measurements

2019 ◽  
Vol 35 (1) ◽  
pp. 85-103 ◽  
Author(s):  
Luc Tien Le

The friction and ripple effects from motor and drive cause a major problem for the robot position accuracy, especially for robots with high gear ratio and for high-speed applications. In this paper we introduce a simple, effective, and practical method to compensate for joint friction of flexible joint robots with joint torque sensing, which is based on a nonlinear disturbance observer. This friction observer can increase the performance of the controlled robot system both in terms of the position accuracy and the dynamic behavior. The friction observer needs no friction model and its output corresponds to the low-pass filtered friction torque. Due to the link torque feedback the friction observer can compensate for both friction moment and external moment effects acting on the link. So it can be used not only for position control but also for interaction control, e.g., torque control or impedance control which have low control bandwidth and therefore are sensitive to ripple effects from motor and drive. In addition, its parameter design and parameter optimization are independent of the controller design so that it can be used for friction compensation in conjunction with different controllers designed for flexible joint robots. Furthermore, a passivity analysis is done for this observer-based friction compensation in consideration of Coulomb, viscose and Stribeck friction effects, which is independent of the regulation controller. In combining this friction observer with the state feedback controller \cite{Albu-Schaeffer2}, global asymptotic stability of the controlled system can be shown by using Lyapunov based convergence analysis. Experimental results with robots of the German Aerospace Center (DLR) validate the practical efficiency of the approach.

Sign in / Sign up

Export Citation Format

Share Document