  Linearising the dynamics of nonlinear mechanical systems is an important and
open research area. In this paper, we adopt a data-driven and feedback control
approach to tackle this problem. A model predictive control architecture is
developed that builds upon data-driven dynamic models obtained using nonlinear
system identification. The overall methodology shows a high degree of
performance combined with significant robustness against imperfect modelling
and extrapolation. These findings are demonstrated using large set of synthetic
experiments conducted on a asymmetric Duffing oscillator and using an
experimental prototype of a high-precision motion system.
