Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach-Reference-Cited by-同舟云学术

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Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach

Published:2022-06-08 Issue: Volume: Page:
ISSN:
Container-title:2022 American Control Conference (ACC)
language:
Short-container-title:

Author:

Kon Johan¹,Bruijnen Dennis²,van de Wijdeven Jeroen³,Heertjes Marcel¹,Oomen Tom¹

Affiliation:

1. Eindhoven University of Technology,Control Systems Technology Group,Departement of Mechanical Engineering,Eindhoven,The Netherlands,5600 MB

2. Philips Engineering Solutions,Eindhoven,The Netherlands,5656 AE

3. ASML,Veldhoven,The Netherlands,5504 DR

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9866948/9867142/09867653.pdf?arnumber=9867653

Reference23 articles.

1. Zero Phase Error Tracking Algorithm for Digital Control

2. Robust inversion-based feedforward controllers for output tracking under plant uncertainty

3. Additive feedforward control with neural networks

4. Neural networks for control systems—A survey

5. Linear motor motion control using a learning feedforward controller

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1. A review on positioning uncertainty in motion control for machine tool feed drives;Precision Engineering;2024-06

2. Physics-guided neural networks for feedforward control with input-to-state-stability guarantees;Control Engineering Practice;2024-04

3. Control-relevant neural networks for feedforward control with preview: Applied to an industrial flatbed printer;IFAC Journal of Systems and Control;2024-03

4. Adaptive learning control of robot manipulators via incremental hybrid neural network;Neurocomputing;2024-02

5. Data-Driven Feedforward Control Design for Nonlinear Systems: A Control-Oriented System Identification Approach;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

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