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Model-free control for continuum robots based on an adaptive Kalman filter

Li, Minhan; Kang, Rongjie; Branson, David T.; Dai, Jian S.

Authors

Minhan Li

Rongjie Kang

Profile image of DAVID BRANSON

DAVID BRANSON DAVID.BRANSON@NOTTINGHAM.AC.UK
Professor of Dynamics and Control

Jian S. Dai



Abstract

Continuum robots with structural compliance have promising potential to operate in unstructured environments. However, this structural compliance brings challenges to the controller design due to the existence of considerable uncertainties in the robot and its kinematic model. Typically, a large number of sensors are required to provide the controller the state variables of the robot, including the length of each actuator and position of the robot tip. In this paper, a model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position. As the Kalman filter operates only with a two-step algebraic calculation in every control interval, the low computational load and real-time control capability are guaranteed. By adding an optimal vector to the control law, buckling of the robot can also be avoided. Through simulation analysis and experimental validation, this control method shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.

Citation

Li, M., Kang, R., Branson, D. T., & Dai, J. S. (2018). Model-free control for continuum robots based on an adaptive Kalman filter. IEEE/ASME Transactions on Mechatronics, 23(1), https://doi.org/10.1109/TMECH.2017.2775663

Journal Article Type Article
Acceptance Date Nov 13, 2017
Online Publication Date Nov 20, 2017
Publication Date Feb 1, 2018
Deposit Date Mar 20, 2018
Journal IEEE/ASME Transactions on Mechatronics
Print ISSN 1083-4435
Electronic ISSN 1941-014X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 1
DOI https://doi.org/10.1109/TMECH.2017.2775663
Public URL https://nottingham-repository.worktribe.com/output/962792
Publisher URL http://ieeexplore.ieee.org/document/8115276/
Contract Date Mar 20, 2018