34 citations · 44 across the 8 of their papers we have counts for
8 papers
Physics-guided gated recurrent units for inversion-based feedforward control
Mingdao Lin, Max Bolderman, Mircea Lazar
Inversion-based feedforward control relies on an accurate model that describes the inverse system dynamics. The gated recurrent unit (GRU), which is a recent architecture in recurr…
Structured physics-guided neural networks for electromagnetic commutation applied to industrial linear motors
Max Bolderman, Mircea Lazar, Hans Butler
Mechatronic systems are described by an interconnection of the electromagnetic part, i.e., a static position-dependent nonlinear relation between currents and forces, and the mecha…
Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors
Daiwei Fan, Max Bolderman, Sjirk Koekebakker +2
Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficien…
Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach
Max Bolderman, Mircea Lazar, Hans Butler
Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (in…
Physics-guided neural networks for feedforward control with input-to-state stability guarantees
Max Bolderman, Hans Butler, Sjirk Koekebakker +5
The increasing demand on precision and throughput within high-precision mechatronics industries requires a new generation of feedforward controllers with higher accuracy than exist…
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 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…
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…