@inproceedings{e899949e0ac848328d5b5d68aa2192d3,
title = "LADRC with Genetic Algorithm based course tracking for autonomous surface vehicle",
abstract = "A new control method independent of plant model is proposed for course tracking control in an autonomous surface vessel. The autopilot design is based on the linear active disturbance rejection control (LADRC) strategy, which actively compensates for dynamic changes in the system and unpredictable external disturbances. The LADRC controller uses an extended state estimator to give an estimation of the general disturbance. The performance of LADRC depends on the quick convergence state observer, bandwidth of which has great influence on the tracking speed. So the optimal fast tracking bandwidth for digital controller is proposed in this paper. And then only one parameter, closed-loop width, is left to adjust. Genetic Algorithm is used in the paper to tune the last parameter closed-loop width. Simulations and experiments are conducted to validate the efficiency and excellent robustness of the above controller and the results support the analysis.",
keywords = "Active Disturbance Rejection, Autonomous Surface Vehicle, Course Tracking Control, Genetic Algorithm",
author = "Bo Wang and Qing Fei",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7553154",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "626--630",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
address = "United States",
}