Reliability is a critical issue in navigation of unmanned aerial vehicles UAVs since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical-grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The observer is obtained from input-output experimental data with the Observer/Kalman Filter Identification OKID method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input-output experimental data. Results are similar to the Kalman filter, but, with the proposedmethod, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.


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    Title :

    Detection of sensor faults in small helicopter UAVs using Observer/Kalman filter identification


    Contributors:

    Published in:

    Publication date :

    2011


    Size :

    20 Seiten, 8 Bilder, 5 Tabellen, 34 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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