  This paper considers optimal input design when the intended use of the
identified model is to construct a feed-forward controller based on measurable
disturbances. The objective is to find a minimum power excitation signal to be
used in system identification experiment, such that the corresponding
model-based feed-forward controller guarantees, with a given probability, that
the variance of the output signal is within given specifications. To start
with, some low order model problems are analytically solved and fundamental
properties of the optimal input signal solution are presented. The optimal
input signal contains feed-forward control and depends of the noise model and
transfer function of the system in a specific way. Next, we show how to apply
the partial correlation approach to closed loop optimal experiment design to
the general feed-forward problem. A framework for optimal input signal design
for feed-forward control is presented and numerically evaluated on a
temperature control problem.
