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 will 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 will 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)


    Publication date :

    2005-01-01


    Size :

    7218233 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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