Grey-box modeling of friction: An experimental case-study
R.H.A. Hensen, G.Z. Angelis, René van de Molengraft, A.G. de Jager, J.J. Kok · 1999
Grey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model 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 iteratively 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.