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Found 13 papers, 0 papers with code
Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control
Motion systems are a vital part of many industrial processes.
Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors
Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics.
Exploring the use of deep learning in task-flexible ILC
To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
On Improved Commutation for Moving-Magnet Planar Actuators
The demand for high-precision and high-throughput motion control systems has increased significantly in recent years.
Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach
To achieve this, we present a model of the system in a lifted space of trajectories, based on which we derive an upperbound on the reference tracking performance.
Physics-guided neural networks for feedforward control with input-to-state stability guarantees
The increasing demand on precision and throughput within high-precision mechatronics industries requires a new generation of feedforward controllers with higher accuracy than existing, physics-based feedforward controllers.
On modal observers for beyond rigid body $H_\infty$ control in high-precision mechatronics
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 position dependent effects that originate from relative actuation and sensing of the moving-body.
Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design
However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance.
LPV sequential loop closing for high-precision motion systems
Increasingly stringent throughput requirements in the industry necessitate the need for lightweight design of high-precision motion systems to allow for high accelerations, while still achieving accurate positioning of the moving-body.
On discretization of continuous-time LPV control solutions
However, the main complication of CT synthesis approaches is the successive implementation of the resulting CT control solutions on physical hardware.
On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization
Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model.
Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system
Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment.
Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors
Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations.
