An online pilot manual control behavior identification method, based on recursive low-order time-series model estimation, is presented and validated using experimental data. Eight participants performed compensatory tracking tasks with time-varying vehicle dynamics, where, at an unpredictable moment during a run, a sudden degradation in dynamics could occur. They were instructed to push a button when they detected a change in dynamics. Two methods to automatically detect the moment when pilot adaptation occurs from online estimated parameter traces are discussed. Results show that pilots are more accurate in detecting changes than either algorithm. But when the algorithms are correct, they are often quicker to detect pilot adaptation than pilots themselves. The presented techniques have potential but need improvements.


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    Title :

    Online Identification and Detection of Manual Control Adaptation with Recursive Time Series


    Contributors:

    Published in:

    Publication date :

    2025-05-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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