Neural network control of spacecraft formation using RISE feedback

Abstract

We address the problem of tracking relative translation in a leader-follower spacecraft formation architecture using Robust Integral of the Sign Error (RISE) based Neural Network (NN) technique. Based on the relative translational dynamic model of the spacecraft formation, RISE is introduced to approximate the dynamics of the follower as well as various practical disturbances. It is shown that the errors of the entire formation closed-loop are asymptotical stability (AS), which takes significant advantage over the typical Uniformly Upper Bounded (UUB) property of most NN controllers in high-precision formation tasks. Finally, numerical simulation is provided to verify the effectiveness of the proposed algorithm. © 2010 Springer-Verlag Berlin Heidelberg.