TY - GEN
T1 - Iterative Learning Control Based on Stretch and Compression Mapping for Trajectory Tracking in Human-robot Collaboration
AU - Xia, Jingkang
AU - Huang, Deqing
AU - Li, Yanan
AU - Zhong, Junpei
N1 - Funding Information:
*This work is supported in part by the Sichuan Science and Technology Program under Grants 2019YFG0345 and 2019YJ0210 and National Natural Science Foundation (NNSF) of China under Grants 51805025, 61773323 and 61733015. (Corresponding author : Y anan Li.)
Publisher Copyright:
© 2020 IEEE.
PY - 2021/1/29
Y1 - 2021/1/29
N2 - This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner's desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner's repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods.
AB - This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner's desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner's repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods.
KW - Human-robot interaction
KW - Iterative learning control
KW - Robotic control
UR - http://www.scopus.com/inward/record.url?scp=85100947996&partnerID=8YFLogxK
U2 - 10.1109/CAC51589.2020.9326665
DO - 10.1109/CAC51589.2020.9326665
M3 - Conference article published in proceeding or book
AN - SCOPUS:85100947996
T3 - Proceedings - 2020 Chinese Automation Congress, CAC 2020
SP - 3905
EP - 3910
BT - Proceedings - 2020 Chinese Automation Congress, CAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Chinese Automation Congress, CAC 2020
Y2 - 6 November 2020 through 8 November 2020
ER -