Journal of Astronautics ›› 2019, Vol. 40 ›› Issue (11): 1322-1331.doi: 10.3873/j.issn.1000-1328.2019.11.008

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Model Predictive Control of Combined Spacecraft Based on Deep Learning

KANG Guo hua, JIN Chen di, GUO Yu jie, QIAO Si yuan   

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2018-09-29 Revised:2019-03-09 Online:2019-11-15 Published:2019-11-25

Abstract:  To solve the problem of the attitude control reconstruction in the combined spacecraft, a predictive model control algorithm based on the convolution neural network is proposed. By the advantage of deep learning in the multi-parameter optimization, this algorithm has the characteristic of human-like behavior of prediction before control. It is suitable for the aerospace application scenarios with low hardware requirements. Firstly the algorithm uses the predictive model to control the combined spacecraft from the initial state to the expected state. Then the state variables in the process are used in the training of the three-layer convolutional neural network. When the training is completed, the convolutional neural network is used to replace the model prediction algorithm to control the combined spacecraft. This reduces the need for hardware performance. The simulation results show that the algorithm can predict the control parameters within five control cycles and can be reduced by about 5 times of hardware calculation time compared with the traditional model prediction algorithm. The attitude control of the combined spacecraft is completed within 30 seconds, and the control accuracy is about 10 -4 order of magnitude.

Key words: Deep learning, Combined spacecraft, Model predictive control (MPC), Convolution neural network (CNN), Attitude control.

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