JIN Chendi, KANG Guohua*, GUO Yujie, QIAO Siyuan
Aiming at the problem of unknown dynamic parameters of the new assembly during the onorbit service, a parameter identification algorithm based on convolution neural network was proposed with the help of deep learning in multiparameter optimization. The algorithm realizes the identification of the combined spacecraft′s multiparameter under the condition of external force and non conservation of linear momentum and angular momentum. A 4layer 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%.
Convolutional Neural Network(CNN)
JIN Chendi, KANG Guohua, GUO Yujie, QIAO Siyuan. Onorbit intelligent identification of combined spacecraft′s inertia parameter based on deep learning[J]. , doi: 10.16708/j.cnki.1000-758X.2019.0003.
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