34 citations · 106 across the 32 of their papers we have counts for
6 papers · 1 filter
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…
Stochastic Model Predictive Control with Dynamic Chance Constraints
Maico Hendrikus Wilhelmus Engelaar, Sofie Haesaert, Mircea Lazar
This work introduces a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic…
Generalized Data-driven Predictive Control
M. Lazar, P. C. N. Verheijen
Data-driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is…
Abstracting Linear Stochastic Systems via Knowledge Filtering
Maico Hendrikus Wilhelmus Engelaar, Licio Romao, Yulong Gao +3
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This…
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…