Inertia matrix identification of combined spacecraft using a deep neural network with optimized network structure

Abstract

After the space robot captures the out-of-control spacecraft, they form a combined spacecraft. For the parameter identification problem of combined spacecraft with complex angular rate measurement noise, deep learning could provide a new solution, due to its ability to extract robust features from noisy data. However, the deep neural network (DNN) has substantial computing resource requirements, which restricts its application in spacecraft systems. To solve the above problems, an inertia matrix identification approach combining a structure optimization strategy of DNN is proposed to identify the inertia matrix in the presence of complex measurement noise using the DNN with optimized structure. The attitude dynamics of combined spacecraft are firstly derived, and the identification problem considering complex measurement noise is described. Based on the dynamics model, the construction process of DNN for accurately identifying the inertia matrix is proposed. A structure optimization strategy of DNN is then designed to optimize the structure of DNN for increasing computing efficiency. This strategy provides a whole set of solutions including the structure selection of the initial DNN, the redundant neurons pruning process, and the end-of-optimization condition. By combining the proposed construction process of DNN, the structure of the DNN is finally optimized by iterating the structured pruning and retraining step. The performance of the proposed DNN with optimized network structure is verified on a testing set with complex measurement noise. The results show that the proposed approach can accurately identify the inertia matrix of combined spacecraft, and meanwhile greatly improve the computing efficiency of DNN.