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In this paper data-based approaches for a robust Fault Detection (FD) of the Air Data Sensors (ADS) including airspeed angles of attack and sideslip are proposed. Experimental Interval Models (IMs) have been considered for coping with modeling uncertainty and for providing interval estimations of the ADS signals. Specifically, a nonlinear-in-the-parameter Neural Network model has been introduced to...
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