The performance of a feedforward controller is primarily determined by the
extent to which it can capture the relevant dynamics of a system. The aim of
this paper is to develop an input-output linear parameter-varying (LPV)
feedforward parameterization and a corresponding data-driven estimation method
in which the dependency of the coefficients on the scheduling signal are
learned by a neural network. The use of a neural network enables the
parameterization to compensate a wide class of constant relative degree LPV
systems. Efficient optimization of the neural-network-based controller is
achieved through a Levenberg-Marquardt approach with analytic gradients and a
pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The
performance of the developed feedforward learning method is validated in a
simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE
Conference on Decision and Control, Singapore, 202