Identification of time-varying inertia tensor of flexible solar sail based on CNN and BiLSTM

Abstract:

To accurately identify the inertia tensor parameters of flexible solar sail spacecraft during on-orbit deployment and improve the accuracy and robustness of the parameter identification model in complex disturbance environments, this paper proposed a data-driven parameter identification method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) hybrid network. This method combined the local feature extraction capability of CNN with the time series data modeling advantage of BiLSTM. First, the paper established the attitude-vibration coupling dynamics model of the flexible solar sail spacecraft and generated massive training data using domain randomization. Second, a CNN-BiLSTM network parameter identification model was constructed and trained using a pre-designed training strategy. Finally, the trained model was applied to identify the inertia tensor of the solar sail under different measurement noise conditions, and the identification results of this model and single models were compared and analyzed. Simulation results demonstrate that the constructed network model has a mean relative error of less than 1% in identifying the system inertia tensor parameters under disturbance-free conditions. In addition, under complex measurement noise conditions with disturbance torques and lower signal-to-noise ratios, the mean relative error of the identification results still does not exceed 1.5%, which is significantly better than that of the CNN and BiLSTM models. The proposed method effectively addresses the shortcomings of single network models, not only accurately identifying the time-varying inertia tensor of the flexible solar sail but also enhancing robustness in complex disturbance environments.