Dynamic Scheduling Optimization for Multi-Satellite Mission Measurement and Control Planning Using Evolutionary Algorithms and Model Predictive Control

This paper presents a dynamic scheduling optimization method for the multi-satellite mission measurement and control planning problem, which is a complex and uncertain optimization problem that involves rationalizing the missions of satellites and allocating limited resources to the measurement and control missions of satellites. The proposed method combines an evolutionary algorithm and a model predictive control framework, which can effectively deal with the complexity and uncertainty of the problem and achieve optimal observation and communication of multiple targets by multiple satellites. The paper develops an information-guided evolutionary algorithm that uses expert knowledge and conflict reduction rules to generate high-quality solution sets, applies a model predictive control framework that performs online adjustment and update based on actual data and feedback mechanism and designs a series of experimental scenarios to compare the performance of different methods in different situations.