5 papers
Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design
Max Bolderman, Mircea Lazar, Hans Butler
Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such…
On discretization of continuous-time LPV control solutions
Yorick Broens, Hans Butler, Roland Tóth
In recent years, the Linear Parameter-Varying (LPV) framework has become increasingly useful for analysis and control of time-varying systems. Generally, LPV control synthesis is p…
On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization
Max Bolderman, Mircea Lazar, Hans Butler
Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics-guided neural networks (PGNN), where a known p…
Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system
Ioannis Proimadis, Yorick Broens, Roland Tóth +1
Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment. However, position tracking accuracy of high precision mechatron…
Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors
Max Bolderman, Mircea Lazar, Hans Butler
Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations. In turn this…