Attitude control of ultra-low orbit satellite based on RBF neural network

Abstract

Ultra-low-orbit satellites have the advantages of high resolution, high efficiency and low launch costs; however, atmospheric drag may lead to complex external interference, and continuous orbital fuel consumption may cause uncertain satellite rotation inertia. In view of the attitude control problem of ultra-low orbit satellite, this paper puts forward an adaptive attitude control method based on RBF neural network, which approaches the ideal slip mode controller through RBF neural network and adjusts neural network parameters according to external disturbance adaptation. The paper is designed to prove the progressive stability of the controller by Lyapunov theory and carried out the simulation verification. The simulation results show that the designed attitude controller can effectively overcome the influence of uncertainty disturbance in the system and improve the accuracy of attitude control.