Control systems for safety-critical applications, including the ones relying on adaptive elements, have to be certified against strict performance and safety requirements. This study presents a practical approach for the design of a neuro-adaptive element with the specific purpose of safely recovering the performance of a reference model in presence of bounded uncertainties. The boundedness of the tracking error vector within an a-priori specified compact domain is enforced by applying robust invariant set analysis to the uncertain linear plant where the adaptive neural contribution is considered as an amplitude-bounded persistent disturbance. In this framework, tracking error requirements are specified via a set of LMI constraints and maximal allowed amplitudes for the adaptive control are computed using a numerical LMI solver. A specific neural network on-line learning and output confinement algorithm is also proposed to keep the adaptive control within selected amplitudes; as a result, the overall closed loop system has a guaranteed worst-case transient response. The proposed approach has been successfully applied to the design of a multi input multi output (MIMO) augmentation adaptive element that improves the performance of a pre-existing tracking controller for a research aircraft model.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Performance-oriented adaptive neural augmentation of an existing flight control system


    Contributors:

    Published in:

    Publication date :

    2011


    Size :

    10 Seiten, 31 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Fault Tolerant Flight Control via Adaptive Neural Network Augmentation

    Rysdyk, R. / Calise, A. / AIAA | British Library Conference Proceedings | 1998





    Neural-Network Augmentation of Existing Linear Controllers

    Manu Sharma / Anthony Calise | AIAA | 2005