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.
@article{arxiv.2309.12722,
title = {Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach},
author = {Johan Kon and Jeroen van de Wijdeven and Dennis Bruijnen and Roland Tóth and Marcel Heertjes and Tom Oomen},
journal= {arXiv preprint arXiv:2309.12722},
year = {2023}
}
Comments
Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 2023