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Parameterized feedforward control is at the basis of many successful controlapplications with varying references. The aim of this paper is to develop anefficient data-driven approach to learn the feedforward parameters for MIMOsystems. To this end, a cost criterion is minimized using a stochastic gradientdescent algorithm, in which both the search direction and step size aredetermined through system experiments. In particular, the search direction ischosen as an unbiased estimate of the gradient which is obtained from a singleexperiment, regardless of the size of the MIMO system. The approach isillustrated using a simulation example, in which it is shown to be superior toa deterministic method in terms of convergence speed and thus experimentalcost.

Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation