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Robust Control Strategy of Gradient Magnetic Drive for Microrobots Based on Extended State Observer.

Authors :
Lu J
Liu Y
Huang W
Bi K
Zhu Y
Fan Q
Source :
Cyborg and bionic systems (Washington, D.C.) [Cyborg Bionic Syst] 2022 Oct 21; Vol. 2022, pp. 9835014. Date of Electronic Publication: 2022 Oct 21 (Print Publication: 2022).
Publication Year :
2022

Abstract

Microrobots have great application potential in the biomedical field, to realize the precision and efficiency of microrobots in vivo is research focus in this field. Microrobots are accompanied by various disturbances in complex environment. These disturbances will affect the motion control of microrobots, resulting in the inability of the micromanipulation tasks to be completed effectively. To this end, a robust motion control method is proposed for precise path tracking of microrobots in this paper. The extended state observer (ESO) is used to estimate the total disturbances and uncertainties of the system. A path tracking controller is designed by combining sliding mode control (SMC) and disturbances compensation, which is used to eliminate the total disturbances of the system and realize the fast and accurate path tracking of microrobots. Finally, the path tracking experiments are implemented in the gradient magnetic field drive system. The experimental results show that the mean absolute error of the path tracking for microrobots in a simulated vascular structure is less than 14  μ m, and the root mean square error is less than 17  μ m by using the robust control method proposed in this paper. Compared with the traditional PID control method, it can better suppress external disturbances and uncertainties of the system and improve the path tracking accuracy of microrobots effectively. It shows stronger anti-interference ability and robustness.<br />Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article.<br /> (Copyright © 2022 Jiawei Lu et al.)

Details

Language :
English
ISSN :
2692-7632
Volume :
2022
Database :
MEDLINE
Journal :
Cyborg and bionic systems (Washington, D.C.)
Publication Type :
Academic Journal
Accession number :
36320320
Full Text :
https://doi.org/10.34133/2022/9835014