This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.


    Access

    Download


    Export, share and cite



    Title :

    Design of uav robust autopilot based on adaptive neuro-fuzzy inference system


    Contributors:


    Publication date :

    2008




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Neuro-Fuzzy Techniques Applied to a Ship Autopilot Design

    Sutton, Robert | Online Contents | 1996



    AIAA-2004-5327 Missile Longitudinal Autopilot Design Using a New Model Following Robust Neuro-Adaptive Controller

    Balakrishnan, S. / Unnikrishnan, N. | British Library Conference Proceedings | 2004


    H~ Robust Autopilot Design

    Desanj, D. S. / Katebi, M. R. / Grimble, M. J. et al. | British Library Conference Proceedings | 1995


    Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

    Maurya, Akhilesh Kumar / Patel, Devesh Kumar | Springer Verlag | 2014