Fault-Tolerant Adaptive Model Inversion Control for Vision-Based Autonomous Air Refueling
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2016 by John Valasek, Douglas Famularo, and Monika Marwaha. The practical autonomous air refueling of unmanned air systemtanker aircraft to unmanned air systemreceiver aircraft will require an integrated relative navigation system and controller that is tolerant to faults. This paper develops and demonstrates a fault-Tolerant structured-Adaptive-model-inversion controller integrated with a reliable relative-position sensor for this autonomous air-refueling scenario using the probe-And-drogue method. The structured-Adaptive-model-inversion controller does not depend on fault-detection information, yet reconfigures and provides smooth trajectory tracking and probe docking in the presence of control-effector failure. The controller also handles parameter uncertainty in the receiver-Aircraft model. In this paper, the controller is integrated with a vision-based relative-position sensor, which tracks the relative position of the drogue, and a reference-Trajectory generator. The feasibility and performance of the controller and integrated system are demonstrated with simulated docking maneuvers with a nonstationary drogue, in the presence of system uncertainties and control-effector failures. The results presented in the paper demonstrate that the integrated controller/sensor systemcan provide successful docking in the presence of systemuncertainties for a specified class of control-effector failures.
