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

Grey-box modeling covers the domain where ive want to use a balanced amount of white-box modeling based on first principles and black-box modeling bas ed on empiricism. The two grey box models presented combine a white-box mod el with a black-box model, i.e., a neural network model and a polytopic mod el that are capable of identifying friction characteristics that are left u nexplained by first principles modeling. In an experimental case-study, both grey-box models are applied to identify a rotating arm subjected to function. An augmented state extended Kalman f ilter is used iteratively and off-line for the, estimation of unknown param eters. For the studied example and defined black-box topologies, little dif ference is observed between the two models. In addition, the applicability of the identified models is illustrated in a model based friction compensat ion control scheme with the objective to linearize the system.