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arXiv · 2309.12722

Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach

Johan Kon·Jeroen van de Wijdeven·Dennis Bruijnen·Roland Tóth·Marcel Heertjes·Tom Oomen
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Abstract

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.

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BibTeXRIS

Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen. 2023-09-22. Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach. https://arxiv.org/abs/2309.12722

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