Title :
Neural Network-Based Distributed Attitude Coordination Control for Spacecraft Formation Flying With Input Saturation
Author :
An-Min Zou ; Kumar, K.D.
Author_Institution :
Dept. of Aerosp. Eng., Ryerson Univ., Toronto, ON, Canada
fDate :
7/1/2012 12:00:00 AM
Abstract :
This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller.
Keywords :
Lyapunov methods; aircraft control; attitude control; distributed control; graph theory; neurocontrollers; numerical analysis; observers; robust control; time-varying systems; variable structure systems; Chebyshev neural networks; Lyapunov-based approach; common time-varying reference attitude; graph theory; input saturation; neural network-based distributed attitude coordination control problem; numerical simulations; quaternion-based distributed attitude coordination control scheme; robust control technique; separation principle theorem; sliding-mode observer; smooth projection algorithm; spacecraft formation flying; Attitude control; Chebyshev approximation; Observers; Polynomials; Quaternions; Space vehicles; Symmetric matrices; Attitude coordination control; Chebyshev neural networks; control input saturation; quaternion; spacecraft formation flying;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2012.2196710