Intelligent fault-tolerant control for the auto-tracking system of LEO satellites using machine learning

Abstract This paper presents an approach based on an intelligent fault-tolerant (FT) mechanism for enhancing a monopulse auto-tracking architecture through the integration of machine learning (ML) techniques. This approach is applied for the case of Oran ground station (GS) communicating in the S/X band frequency range to ensure system robustness during faulty events. The latter are due to the degradation or sudden failure of neuralgic radio frequency (RF) components, with an emphasis on the low-noise amplifier (LNA). To address these vulnerabilities, a probabilistic likelihood function is defined, and its statistical parameters are optimised using the grey wolf optimiser (GWO) in order to provide a means of automatic switchover to the ML tracking signal receiver (ML-TSR) control mode upon anomaly detection. Three ML algorithms, namely eXtreme Gradient Boosting (XGBoost), decision trees, and K-nearest neighbors (KNN), were implemented and evaluated using a database spanning eight years (2017–2024), including the operational parameters of the ALSAT-1B GS. The experimental results show that the KNN model achieves higher accuracy than the other approaches. A validation process was then conducted during an actual satellite pass encompassing a nominal phase followed by a GS LNA failure phase. During the nominal phase, a root mean square error (RMSE) of only 0.46° in azimuth and 0.64° in elevation was observed. Activation of our recovery mechanism during the second phase demonstrated that the robustness of the likelihood function, combined with the accuracy of the ML-TSR controller, ensures mission continuity without data loss, leading to four successful image downloads.