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

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 optimizati...

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Bibliographic Details
Published in:International journal of aerospace engineering 2022-04, Vol.2022, p.1-16
Main Authors: Wang, Xin, Wang, Guogang, Zheng, Hongru
Format: Article
Language:English
Subjects:
Adaptive control
Aerospace engineering
Algorithms
Automatic control
Chemical propulsion
Control algorithms
Control methods
Control theory
Design
Design optimization
Feasibility
Ground stations
Kalman filters
Neural networks
Phase control
Propulsion systems
Radial basis function
Remote sensing
Satellite attitude control
Satellite constellations
Satellites
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Summary: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.
ISSN:1687-5966
1687-5974
DOI:10.1155/2022/7153667