Articles | Volume 13, issue 1
https://doi.org/10.5194/ms-13-427-2022
https://doi.org/10.5194/ms-13-427-2022
Research article
 | 
04 May 2022
Research article |  | 04 May 2022

Payload-adaptive iterative learning control for robotic manipulators

Kaloyan Yovchev and Lyubomira Miteva

Related subject area

Subject: Dynamics and Control | Techniques and Approaches: Mathematical Modeling and Analysis
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Cited articles

Arimoto, S., Kawamura, S., and Miyazaki, F.: Iterative learning control for robot systems, in: Proceedings of IECON, Tokyo, Japan, 1984a (in Japanese). 
Arimoto, S., Kawamura, S., and Miyazaki, F.: Bettering operation of robots by learning, J. Robotic Syst., 1, 123–140, https://doi.org/10.1002/rob.4620010203, 1984b. 
Delchev, K.: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems, Archives of Control Sciences, 22, 371–384, https://doi.org/10.2478/v10170-011-0036-9, 2012. 
Eaton, J. W., Bateman, D., Hauberg, S., and Wehbring, R.: GNU Octave version 5.2.0 manual: a high-level interactive language for numerical computations, https://octave.org/doc/octave-5.2.0.pdf (last access: 29 April 2022), 2019. 
Heizinger, G., Fenwick, D, Paden, B., and Miyazaki, F.: Robust Learning Control, in: Proc. of 28th Conference on Decision and Control, 13–15 December 1989, Tampa, FL, https://doi.org/10.1109/CDC.1989.70152, 1989. 
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Short summary
Industrial robots are required to perform a given task repetitively with high tracking precision. Iterative learning control (ILC) calculates the tracking error of each iteration and corrects the output control signals in accordance with a predefined learning operator. This research considers the changes of the dynamics characteristics when the robot has different types of payload. It provides an approach for adaptation of the ILC to the specific payload to achieve faster convergence.