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Robotics 2018, 7(3), 35; https://doi.org/10.3390/robotics7030035

Calibration of UR10 Robot Controller through Simple Auto-Tuning Approach

1
Department of Electromechanics, University of Antwerp, Op3Mech, Groenenborgerlaan 171, 2020 Antwerp, Belgium
2
Department of Automation, Technical University of Cluj-Napoca, Memorandumului Street, No. 28, 400114 Cluj Napoca, Romania
3
Flanders Make, EEDT Group, Research Group on Dynamical Systems and Control (DySC), Department of Electrical Energy, Metals, Mechanical Constructions and Systems (EEMMeCS), Ghent University, Technologiepark 914, 9052 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Received: 15 May 2018 / Revised: 2 July 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
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Abstract

This paper presents a calibration approach of a manipulator robot controller using an auto-tuning technique. Since the industry requires machines to run with increasing speed and precision, an optimal controller is too demanding. Even though the robots make use of an internal controller, usually, this controller does not fulfill the user specification with respect to their applications. Therefore, in order to overcome the user requirements, an auto-tuning method based on a single sine test is employed to obtain the optimal parameters of the proportional–integral–derivative PID controller. This approach has been tested, validated and implemented on a UR10 robot. The experimental results revealed that the performances of the robot increased when the designed controller, using the auto-tuning technique, was employed. View Full-Text
Keywords: PID control; auto-tuning method; robot; calibration PID control; auto-tuning method; robot; calibration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Copot, C.; Muresan, C.; Ionescu, C.-M.; Vanlanduit, S.; De Keyser, R. Calibration of UR10 Robot Controller through Simple Auto-Tuning Approach. Robotics 2018, 7, 35.

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