Abstract This paper introduces a novel approach to enhance the fault-tolerant control of flight vehicles by incorporating a gated recurrent unit (GRU) neural network within a gain-scheduled framework. Gain scheduling, a well-established technique for achieving active fault-tolerant control, enables the selection of control gains from a predefined set based on specific faults. An improved terminal sliding mode controller is derived, and appropriate parameters are chosen to formulate a gain-scheduled fault-tolerant controller. Based on it, training data is generated by comprehensive simulations on a Winged-Cone configuration flight vehicle. The GRU neural network architecture is designed and trained to function as the flight controller. The effectiveness of the proposed GRU network controller is demonstrated through a series of simulations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A GRU network framework towards fault-tolerant control for flight vehicles based on a gain-scheduled approach


    Contributors:
    Yang, Binxiang (author) / Lu, Pingli (author) / Du, Changkun (author) / Cao, Fangfei (author)


    Publication date :

    2024-02-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Gain-scheduled flight control law for flexible aircraft: a practical approach

    Puyou, Gulhem / Berard, Caroline | Tema Archive | 2009



    Fuzzy Gain-Scheduled H-infinity Flight Control Law Design

    Oosterom, Marcel / Bergsten, Pontus / Babuska, Robert | AIAA | 2002