no code implementations • 18 Jan 2024 • Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen
Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e. g., through solving Linear Matrix Inequalities.
no code implementations • 22 Sep 2023 • Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen
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.
no code implementations • 14 Mar 2023 • Johan Kon, Naomi de Vos, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction.
no code implementations • 26 Sep 2022 • Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
Unknown nonlinear dynamics often limit the tracking performance of feedforward control.
no code implementations • 10 Jan 2022 • Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics.