Attitude synchronization of spacecraft formation using neural network

We address the problem of high-precision attitude synchronization in a leader-follower spacecraft formation architecture using robust integral of the sign of the error (RISE) based Neural Network (NN) technique. Based on the relative attitude dynamic model of the spacecraft formation, RISE is introduced to approximate the dynamics of the follower as well as various external disturbances. It is shown that the errors of the entire formation closed-loop are Asymptotical Stable (AS), which possesses significant advantage over the typical Uniformly Upper Bounded (UUB) property of most NN controllers in high-precision attitude synchronization tasks. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm.