基于深度学习的航天器组合体惯性参数在轨智能辨识

针对在轨服务过程形成新组合体的动力学参数未知的问题，借助深度学习在多参数寻优上的优势，提出了一种基于卷积神经网络的智能参数辨识算法，实现在外力作用下，线动量和角动量不守恒条件下的航天器组合体多参数辨识。利用卷积神经网络权值共享的特点，设计4层卷积神经网络，通过短时间内对大量特定存储形式的状态数据的训练，实现航天器组合体多参数快速高精度辨识。利用数学仿真试验对算法的可行性进行验证，结果表明：在24s内，质量与质心位置收敛;1190s内，惯量参数收敛，辨识精度在3%以内。说明所提方法在外界随机干扰力和力矩影响下能准确快速辨识出航天器组合体质量、质心位置和惯量矩阵。

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

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%.