A Summary of Research on Optimization of RVD Methods Based on Human Control Docking Knowledge Acquisition

Abstract

The intelligence of space control, especially the intelligence of docking tasks, is a trend. From the past performance of manual and automatic docking, the performance of automatic docking in terms of accuracy, smoothness and maneuverability is not as good as manual docking. The control strategy is extracted from the training data of the astronauts’ manual docking, and it is transformed into experience and knowledge by machine learning technology, integrated into the control law, improving accuracy and stability, improving the autonomy and precision of space rendezvous and docking, and achieving docking. Realize the controller to achieve the effect of people’s cognition, judgment and decision on the task. In addition, the complexity and diversity of spatial tasks put forward new requirements for docking technology. For new similar and different tasks, deep migration learning methods are used to realize online learning and final learning based on existing knowledge. The ability of the controller to be generalized to similar but different tasks is a direct manifestation of intelligence.