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CONTROL OF UNMANNED SURFACE VEHICLE ALONG THE DESIRED TRAJECTORY USING IMPROVED LINE OF SIGHT AND ESTIMATED SIDESLIP ANGLE

Abstract

In order to improve the accuracy and robustness of path following control for an Unmanned Surface Vehicle (USV) suffering from unknown and complex disturbances, a variable speed curve path following a control method based on an extended state observer was proposed. Firstly, the effect of the environmental disturbances on the USV is equivalent to an unknown and time-varying sideslip angle, and the sideslip angle is estimated by using the extended state observer (ESO) and compensated in the Line of Sight (LOS) guidance law. Secondly, based on the traditional LOS guidance law, the design of the surge velocity guidance law is added to enable the USV to self-adjust the surge velocity according to the curvature of the curve path, thus further improving the tracking accuracy. Finally, the heading and speed controller of the USV is designed by using a sliding mode control to track the desired heading and speed accurately, and then the path following control of the USV’s curve path is realised. Simulation results verify the effectiveness of the proposed method.

Keywords:

USV, curve path following control, surge velocity guidance law, extended state observer, sliding mode control

Details

Issue
Vol. 28 No. 2(110) (2021)
Section
Latest Articles
Published
15-07-2021
DOI:
https://doi.org/10.2478/pomr-2021-0017
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Ligang Li

    China University of Petroleum
  • Zhiyuan Pei

    China University of Petroleum
  • Jiucai Jin

    First Institute of Oceanography Ministry of Natural Resources
  • Yongshou Dai

    China University of Petroleum

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