  Parameterized feedforward control is at the basis of many successful control
applications with varying references. The aim of this paper is to develop an
efficient data-driven approach to learn the feedforward parameters for MIMO
systems. To this end, a cost criterion is minimized using a stochastic gradient
descent algorithm, in which both the search direction and step size are
determined through system experiments. In particular, the search direction is
chosen as an unbiased estimate of the gradient which is obtained from a single
experiment, regardless of the size of the MIMO system. The approach is
illustrated using a simulation example, in which it is shown to be superior to
a deterministic method in terms of convergence speed and thus experimental
cost.
