Traditional fixed-gain control has proven to be unsuccessful to deal with complex, strongly nonlinear, uncertain, and changing systems such as a damaged aircraft. Control systems with components that can adapt toward changes in the plant, e.g., using a neural network, have been actively investigated as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). However, neuro-adaptive controllers have not been used in safety-critical applications, because performance and safety guarantees cannot be provided at development time - a major prerequisite for safety certification (e.g., by the FAA or NASA). In this paper, we describe our approach toward V&V of neuro-adaptive controllers. We have developed tools which dynamically estimate the neural network performance and safety envelope, using a Bayesian approach. We discuss our V&V approach, the tool architecture and simulation experiments within NASA's IFCS (intelligent flight control system) project.


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    Title :

    Toward verification and validation of adaptive aircraft controllers


    Contributors:
    Schumann, J. (author) / Gupta, P. (author) / Jacklin, S. (author)

    Published in:

    Publication date :

    2005


    Size :

    6 Seiten, 11 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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