Abstract: The moment of inertia is a critical parameter in spacecraft attitude control systems, and its accurate and rapid identification is essential for achieving high-precision attitude control. However, in practical space missions, the moment of inertia often exhibits strongly nonlinear and highly dynamic time-varying characteristics, posing significant challenges for accurate estimation. To meet the demands for autonomous and efficient on-orbit identification, this paper proposes a hybrid data–physics-driven approach for moment of inertia estimation and in-orbit model updating. First, a physics-based model is established to capture the time-varying characteristics of the moment of inertia caused by fuel consumption and appendage deployment, and high-confidence training data are generated using a domain randomization technique. Second, a novel Bi-GRU and attention-enhanced neural network is proposed for accurate identification of time-varying inertia, and a noise-adversarial training scheme is designed based on observation disturbance modeling to ensure robustness under complex noise conditions. Then, an onboard lightweight model update strategy is developed, incorporating pseudo-label supervision and physics-constrained loss to enable efficient online parameter updates of the offline model. Finally, the proposed identification strategy, integrating both data-driven and physics-based models, is embedded into the attitude control feedback loop, forming a closed-loop autonomous onboard operation paradigm linking the control module, data buffer, and identification module. Simulation results show that the proposed method achieves inertia estimation errors within 1% and attitude control accuracy better than 1° under limited data storage and communication delay conditions, demonstrating strong potential for real-world onboard applications.

Key words: Spacecraft, Time-varying moment of inertia, On-orbit identification, Neural Network, Data-driven approach