Grey-box modeling of friction : an experimental case-study

Grey/box modeling covers the domain where we want to use an balanced amount of white/box modeling based on first principles and black/box modeling based on empiricism. The two grey/box models presented combine a white/box model with a black/box model, i.e., a neural network model and a polytopic model that are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case/study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used interatively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models. In addition, the applicability of the identified models is illustrated in a model based friction compensation control scheme with the objective to linearize the system.