Articles | Volume 13, issue 1
Mech. Sci., 13, 427–436, 2022

Special issue: Advances in Service and Industrial Robotics – RAAD2021

Mech. Sci., 13, 427–436, 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
Analysis of divergent bifurcations in the dynamics of wheeled vehicles
Vladimir Verbitskii, Vlad Lobas, Yevgen Misko, and Andrey Bondarenko
Mech. Sci., 13, 321–329,,, 2022
Short summary
Study on multi-degree of freedom dynamic vibration absorber of the car body of high-speed trains
Yu Sun, Jinsong Zhou, Dao Gong, and Yuanjin Ji
Mech. Sci., 13, 239–256,,, 2022
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Review article: A comprehensive review of energy management strategies for hybrid electric vehicles
Yuzheng Zhu, Xueyuan Li, Qi Liu, Songhao Li, and Yao Xu
Mech. Sci., 13, 147–188,,, 2022
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A new type of auxiliary structure for tunnel-surrounding rock support – composite cantilever support structure
Jun Qu, Qilong Xue, Shixin Bai, and Yazhe Li
Mech. Sci., 13, 89–99,,, 2022
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Establishment and analysis of nonlinear frequency response model of planetary gear transmission system
Hao Dong, Yue Bi, Zhen-Bin Liu, and Xiao-Long Zhao
Mech. Sci., 12, 1093–1104,,, 2021
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Cited articles

Arimoto, S., Kawamura, S., and Miyazaki, F.: Bettering operation of robots by learning, J. Robotic Syst., 1, 123–140,, 1984b. 
Delchev, K.: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems, Archives of Control Sciences, 22, 371–384,, 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, (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,, 1989. 
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.