  Feedforward control is a viable option for enhancing the response time and
control accuracy of a wide variety of systems. Nevertheless, it is not able to
compensate for the effects produced by modeling errors or disturbances. A
solution to improve the feedforward performance is the use of an adaptation law
that modifies the parameters of the feedforward control. In the case where
real-time feedback is not possible, a solution is a run-to-run numerical
optimization method that is fed with a cost based on a measured signal.
Although the effectiveness of this approach has been demonstrated, its
performance is hindered by slow convergence. In this paper, we present an
algorithm based on Pattern Search and Adaptive Coordinate Descent methods that
makes use of the sensitivity of the feedforward controller to its parameters so
that the convergence speed improves significantly. Like many algorithms, this
is a local strategy so the algorithm might converge to a local minimum.
Therefore, we present two versions, one without a learning rate and one with
it. To compare them and to demonstrate the effectiveness of the algorithm,
simulated results are shown on a well-known control problem in
electromechanics: the soft-landing control of electromechanical switching
devices.
