@inproceedings{9569378683f64139befd1f0cad08f1e4,
title = "Adaptive Fault-Tolerant Control of Rigid Body Using RBF Neural Networks",
abstract = "In this paper, an adaptive fault-tolerant attitude control problem is presented of rigid body using radial basis function neural network (RBF NN). The faults we considered are that the thrusters of the rigid might partially or totally lose power. The uncertainty of the system produced by the external disturbances, unknown inertia matrix and thrusters failures are approximated by RBF NN. It is proved that the control method can guarantee that all the signals of the closed-loop system are bounded. Simulation results are presented to demonstrate that the controller is available in achieving high attitude control with external disturbances, inertia uncertainty and thrusters failures.",
keywords = "Adaptive control, Attitude tracking, Fault-tolerant control, Radial basis function neural network (RBF NN), Sliding mode control",
author = "Baoyu Huo and Yuanqing Xia and Senchun Chai and Pen Shi",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",
year = "2015",
month = mar,
day = "2",
doi = "10.1109/WCICA.2014.7052887",
language = "English",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "March",
pages = "1185--1190",
booktitle = "Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014",
address = "United States",
edition = "March",
}