34 citations · 53 across the 14 of their papers we have counts for
15 papers
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…
Exploring the use of deep learning in task-flexible ILC
Anantha Sai Hariharan Vinjarapu, Yorick Broens, Hans Butler +1
Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforw…
On Improved Commutation for Moving-Magnet Planar Actuators
Yorick Broens, Hans Butler, Roland Tóth
The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining p…
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…
On modal observers for beyond rigid body control in high-precision mechatronics
Yorick Broens, Hans Butler, Roland Tóth
The ever increasing need for performance results in increasingly rigorous demands on throughput and positioning accuracy of high-precision motion systems, which often suffer from p…
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…
LPV sequential loop closing for high-precision motion systems
Yorick Broens, Hans Butler, Roland Tóth
Increasingly stringent throughput requirements in the industry necessitate the need for lightweight design of high-precision motion systems to allow for high accelerations, while s…
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…
On the Use of the Smith-McMillan Form in Decoupling System Dynamics
Clarisse Pétua Bosman Barros, Hans Butler, Roland Tóth
In this paper, the use of the Smith-McMillan form in decoupling multiple-input multiple-output system dynamics is analyzed. In short, from a transfer matrix plant model one can obt…
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…
A Method to Guarantee Local Convergence for Sequential Quadratic Programming with Poor Hessian Approximation
Tuan T. Nguyen, Mircea Lazar, Hans Butler
Sequential Quadratic Programming (SQP) is a powerful class of algorithms for solving nonlinear optimization problems. Local convergence of SQP algorithms is guaranteed when the Hes…
An Algorithm for Solving Quadratic Optimization Problems with Nonlinear Equality Constraints
Tuan T. Nguyen, Mircea Lazar, Hans Butler
The classical method to solve a quadratic optimization problem with nonlinear equality constraints is to solve the Karush-Kuhn-Tucker (KKT) optimality conditions using Newton's met…