Satellite Mission Planning Based On Intelligent Optimization Algorithms

With the vigorous development of China’s aerospace technology,the scale and diversity of missions to be performed by satellites continue to increase.At the same time,our nation lacks top-level decision-making capabilities at the mission planning level,resulting in low planning efficiency and waste of satellite resources.Therefore,it is necessary to conduct mission planning.In this context,the study of satellite mission planning is of great significance.This dissertation focuses on the task planning of agile observation satellite(AOS)and on-orbit service satellite(OSS)and proposes mission planning algorithms for different scenarios.The main research contents are as follows:Aiming at the task pre-planning and re-planning of the AOS in observing ground point targets,the time constraints,dynamic constraints,and other constraints imposed by the satellites in this process are analyzed.The most profitable satellites in the observation process are considered.The mathematical model of mission planning for AOS is established based on optimal energy and energy requirements during attitude maneuvering.The tabu search-simulated annealing genetic algorithm(TSSAGA)is designed to solve the satellite mission pre-planning problem,and the tabu search-simulated annealing mutation method is proposed.This method combines the advantages of the tabu search algorithm and the simulated annealing algorithm in the optimization process,improves the mutation effect of the algorithm,and improves the search of the algorithm.Aiming at the problem of mission re-planning of the agile observation satellite to observe ground point targets,the adaptive mutation genetic algorithm(AMGA)is proposed.By designing an adaptive adjustment mechanism of mutation rate,the convergence speed of the optimization solution process is accelerated.Simulation experiments verify the effectiveness of the proposed TSSAGA and AMGA.Aiming at the mission planning problem of a single OSS for multiple target satellite in space,the orbital dynamics constraints,fuel constraints,and other constraints on the satellite are analyzed.In the on-orbit service process,energy optimization and time optimization are required for tasks,and a mathematical model of the on-orbit service mission planning problem is established.The whale optimization algorithm(WOA)is applied to optimize this model.Further,for the task planning problem of multiple service stars under mission constraints to serve multiple target stars in orbit,the constraints of satellites are analyzed,and a mathematical model of this problem is established.In order to save the fuel consumption of the service satellite in the orbit transfer process and improve the efficiency of mission planning,the neighbor search-based genetic algorithm(NSGA)is proposed.This algorithm uses a mutation method based on neighborhood search,which can significantly save the fuel consumption of the service star during the orbit transfer process under the premise of the same planning efficiency.Simulation experiments verify the effectiveness of the WOA and the proposed NSGA.