Next Articles

 Onorbit intelligent identification of combined spacecraft′s inertia parameter based on deep learning

JIN  Chendi, KANG  Guohua*, GUO  Yujie, QIAO  Siyuan   

  1. MSRC,College of Astronautics,Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China
  • Received:2018-07-20 Revised:2018-08-29 Online:2019-04-25 Published:2018-12-28

Abstract: Aiming at the problem of unknown dynamic parameters of the new assembly during the onorbit service, a parameter identification algorithm based on convolution neural network was proposed with the help of deep learning in multiparameter optimization. The algorithm realizes the identification of the combined spacecraft′s multiparameter under the condition of external force and non conservation of linear momentum and angular momentum. A 4layer convolution neural networks was designed by using the characteristic of the weight sharing of the convolution neural network. The identification of inertial parameters with high precision was achieved by plenty of training of state data in a specific form of storage in a short time. The feasibility of the convolution neural network algorithm was proved by simulation calculation. The results show that the proposed method can accurately and quickly identify the mass, centroid position and inertia matrix of combined spacecraft under the influence of external random force and moment, the identification accuracy is within 3%.

Key words: deep learning, combined spacecraft, inertia parameter, onorbit identification, Convolutional Neural Network(CNN)