Autonomous Phase Control Combining EKF and Adaptive Neural Network for Remote Sensing Satellites

Abstract

In this paper, a novel, effective, and feasible autonomous phase control algorithm based on the extended Kalman filtering (EKF) and neural network is firstly developed for addressing the problems of configuration maintenance of remote sensing satellite constellation. A balanced moment arm optimization method is employed to design the installation structure layout of the chemical propulsion system in the satellite. On that basis, an autonomous orbit control strategy is presented for controlling the phase of satellites, and the EKF algorithm is utilized to determine the orbit used to calculate the satellite phase. A radial basis function (RBF) neural network-based attitude control method is proposed to solve the attitude disturbance problem in the course of the phase control, and the RBF neural network is utilized to approximate the coupling torque of the orbit control. The simulation results demonstrate the feasibility of the designed automatic phase control strategy of the satellite.