Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.


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    Title :

    Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation


    Contributors:

    Conference:

    Infotech@Aerospace 2012 ; 2012 ; Garden, Grove, CA, United States


    Publication date :

    2012-06-18


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English





    Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

    He, Yuning / Lee, Herbert K.H. / Davies, Misty | AIAA | 2012


    2012-2471 Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

    He, Y. / Lee, H.K.H. / Davies, M. et al. | British Library Conference Proceedings | 2012