Satellite formation maintaining method based on intelligent optimization prediction control

Abstract

The invention discloses a satellite formation maintaining method based on intelligent optimization prediction control. The method comprises the following steps that S1) a J2 perturbation considered satellite formation relative dynamics model is introduced; S2) an intelligent prediction controller is designed, and combines an adaptive master-slave parallel genetic algorithm with a traditional prediction control strategy; and S3) the intelligent prediction controller is used to optimize and solve the control quantity in a rolling way, and thus, the satellite formation is controlled. According tothe method, interference of J2 perturbation is taken into consideration, prediction control parameters are adjusted in real time to improve the control performance of the prediction controller, and further the satellite formation is controlled precisely in real time.

Description

technical field [0001] The invention relates to a satellite formation maintenance method based on intelligent optimization predictive control, and belongs to the technical field of satellite formation maintenance control. Background technique [0002] In recent years, with the continuous proposal of new space missions, the traditional single large satellite can no longer meet the needs of human beings for space exploration. Because of its strong flexibility and high reliability, satellite formation is playing an increasingly important role in space exploration. However, satellites are inevitably affected by perturbation or uncertain factors, which can easily cause the formation of satellite formation to diverge. Therefore, how to maintain the integrity of the formation configuration through control technology is the key to realizing the overall function of the formation. [0003] In the past ten years, many experts and scholars have designed the control strategy of satelli...

AI Technical Summary

Problems solved by technology

It is worth noting that traditional predictive control parameters are determined based on experience or simulation debugging. This process is complex and time-consuming, and it is impossible to determine whether the parameters are optimal.

At the same time, when the traditional predictive controller works, the parameters are fixed, and the static performance and dynamic performance of the system cannot be considered.

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