Explainable AI-Enhanced Supervisory Control for High-Precision Spacecraft Formation
This work addresses dynamic uncertainties and disturbances in space missions for applications like X-ray observation, though it appears incremental as it combines existing methods like deep neural networks and timed automata.
The paper tackles the problem of optimizing high-precision spacecraft formation for a virtual telescope mission by integrating AI and supervisory control, resulting in reduced energy consumption and improved mission accuracy.
We use artificial intelligence (AI) and supervisory adaptive control systems to plan and optimize the mission of precise spacecraft formation. Machine learning and robust control enhance the efficiency of spacecraft precision formation of the Virtual Telescope for X-ray Observation (VTXO) space mission. VTXO is a precise formation of two separate spacecraft making a virtual telescope with a one-kilometer focal length. One spacecraft carries the lens and the other spacecraft holds the camera to observe high-energy space objects in the X-ray domain with 55 milli-arcsecond angular resolution accuracy. Timed automata for supervisory control, Monte Carlo simulations for stability and robustness evaluation, and integration of deep neural networks for optimal estimation of mission parameters, satisfy the high precision mission criteria. We integrate deep neural networks with a constrained, non-convex dynamic optimization pipeline to predict optimal mission parameters, ensuring precision mission criteria are met. AI framework provides explainability by predicting the resulting energy consumption and mission error for a given set of mission parameters. It allows for transparent, justifiable, and real-time trade-offs, a capability not present in traditional adaptive controllers. The results show reductions in energy consumption and improved mission accuracy, demonstrating the capability of the system to address dynamic uncertainties and disturbances.