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Integrated sliding mode control with input restriction, output feedback and repetitive learning for space robot with flexible-base, flexible-link and flexible-joint

Published online by Cambridge University Press:  10 October 2022

Xiaodong Fu
Affiliation:
School of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang, 330013 Jiangxi, China School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108 Fujian, China
Haiping Ai*
Affiliation:
School of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang, 330013 Jiangxi, China School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108 Fujian, China
Li Chen
Affiliation:
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108 Fujian, China
*
*Corresponding author. E-mail: ahpwuhan@163.com

Abstract

In the control of space robots, flexible vibrations exist in the base, links and joints. When building a motion control scheme, the following three aspects should be considered: (1) the complexity in dynamic modeling; (2) the low accuracy of motion control and (3) the simultaneous suppression of multiple flexible vibrations. In this paper, we propose a motion vibration integrated saturation control scheme. First, the dynamic model of space robot with flexible-base, flexible-link and flexible-joint is established according to the assumed modes method and Lagrange equation. Second, singular perturbation theory is used to decompose the model into two subsystems: a slow subsystem containing the rigid motions of base and joints as well as the vibration of links, and a fast subsystem containing vibrations of base and joints. Third, an integrated sliding mode control with input restriction, output feedback and repetitive learning (ISMC-IOR) is designed, which can track the desired trajectories of base and joints with −3 orders of magnitude accuracy, while suppressing the multiple flexible vibrations of base, links and joints 50%–80% and 37% performance improvement over ISMC-IOR-NV were achieved. Finally, the algorithm is verified by simulations.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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