Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed. In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and nonlinear in the parameters, and how the identification method described in Ref. [1] can be used to identify them when all states are measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque, and temperature. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.
Duality for the Robust Sum of Functions
