  Feedforward controllers typically rely on accurately identified inverse
models of the system dynamics to achieve high reference tracking performance.
However, the impact of the (inverse) model identification error on the
resulting tracking error is only analyzed a posteriori in experiments.
Therefore, in this work, we develop an approach to feedforward control design
that aims at minimizing the tracking error a priori. To achieve this, we
present a model of the system in a lifted space of trajectories, based on which
we derive an upperbound on the reference tracking performance. Minimization of
this bound yields a feedforward control-oriented system identification cost
function, and a finite-horizon optimization to compute the feedforward control
signal. The nonlinear feedforward control design method is validated using
physics-guided neural networks on a nonlinear, nonminimum phase mechatronic
example, where it outperforms linear ILC.
