Least square based ensemble deep learning for inertia tensor identification of combined spacecraft

Abstract

The high accurate identification of inertia tensor of combined spacecraft, which is composed of a servicing spacecraft and a target, is necessary to perform attitude control. Due to the uncertainty of the operating environments of combined spacecraft, the measurement noise of the angular rate may be very complex and will seriously influence the identification accuracy. This paper proposes a least square based weighted ensemble deep learning method to realize a highly accurate identification for the inertia tensor of combined spacecraft in complex operating environments. In this method, a single deep neural network regression model is firstly constructed as an individual model for the ensemble deep learning, and then is trained by enough training data and a designed training strategy. After obtaining a certain number of accurate and diverse single models, all the outputs of single models are combined by several linear functions. A least square based ensemble method for regression is developed to automatically determine the optimal weights of these linear functions. The identification performance of the proposed ensemble deep learning method is tested using two testing sets with different ranks of measurement noises and is compared with other identification methods. The results demonstrate that the proposed ensemble deep learning method has a significant advantage compared with other methods for identifying inertia tensor.