Iterative Learning Control for Milling with Industrial Robots in Advanced Manufacturing
(2014)Department of Automatic Control
- Abstract
- The demand of today for advanced manufactured parts with high precision is due to the increasing complexity of technologies. The parts are typically made by CNC (Computer Numerical Control) machines, which are expensive and comparably big. By using industrial robots that are significantly cheaper, reduced costs can be achieved, which is particularly beneficial for small and medium enterprises. Robots are, however, less stiff and strong and are less accurate compared to the CNC machines.
In this thesis, the idea was that by designing a controller, this could be compensated for so that the robot could perform machining with high precision. The research made in this thesis was part of the EU co-funded research project COMET.
The robot... (More) - The demand of today for advanced manufactured parts with high precision is due to the increasing complexity of technologies. The parts are typically made by CNC (Computer Numerical Control) machines, which are expensive and comparably big. By using industrial robots that are significantly cheaper, reduced costs can be achieved, which is particularly beneficial for small and medium enterprises. Robots are, however, less stiff and strong and are less accurate compared to the CNC machines.
In this thesis, the idea was that by designing a controller, this could be compensated for so that the robot could perform machining with high precision. The research made in this thesis was part of the EU co-funded research project COMET.
The robot task was to repeatedly mill parts with improved results. Iterative Learning Control (ILC) was therefore chosen as a suitable control strategy for this task. It uses the position error from previous iterations and adds it to the control signal to converge the output to successful results. Results showed that when using ILC for tracking paths, the position error could be reduced with approximately 11-20% in x, y, and z directions after one iteration. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4462212
- author
- Daun, Thomas
- supervisor
- organization
- year
- 2014
- type
- H3 - Professional qualifications (4 Years - )
- subject
- ISSN
- 0280-5316
- other publication id
- ISRN LUTFD2/TFRT--5942--SE
- language
- English
- id
- 4462212
- date added to LUP
- 2014-06-13 11:18:16
- date last changed
- 2014-06-13 11:18:16
@misc{4462212,
abstract = {{The demand of today for advanced manufactured parts with high precision is due to the increasing complexity of technologies. The parts are typically made by CNC (Computer Numerical Control) machines, which are expensive and comparably big. By using industrial robots that are significantly cheaper, reduced costs can be achieved, which is particularly beneficial for small and medium enterprises. Robots are, however, less stiff and strong and are less accurate compared to the CNC machines.
In this thesis, the idea was that by designing a controller, this could be compensated for so that the robot could perform machining with high precision. The research made in this thesis was part of the EU co-funded research project COMET.
The robot task was to repeatedly mill parts with improved results. Iterative Learning Control (ILC) was therefore chosen as a suitable control strategy for this task. It uses the position error from previous iterations and adds it to the control signal to converge the output to successful results. Results showed that when using ILC for tracking paths, the position error could be reduced with approximately 11-20% in x, y, and z directions after one iteration.}},
author = {{Daun, Thomas}},
issn = {{0280-5316}},
language = {{eng}},
note = {{Student Paper}},
title = {{Iterative Learning Control for Milling with Industrial Robots in Advanced Manufacturing}},
year = {{2014}},
}