Sampled-data self-learning observer based attitude tracking control against sensor-actuator faults

Abstract

This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence of sensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy, highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.