An intelligent approach for smart material actuator modelling of the actuation lines in a morphing wing system is presented, based on adaptive neuro-fuzzy inference systems. Four independent neuro-fuzzy controllers are created from the experimental data using a hybrid method — a combination of back propagation and least-mean-square methods — to train the fuzzy inference systems. The controllers' objective is to correlate each set of forces and electrical currents applied on the smart material actuator to the actuator's elongation. The actuator experi-mental testing is performed for five force cases, using a variable electrical current. An integrated controller is created from four neuro-fuzzy controllers, developed with Matlab/Simulink software for electrical current increases, constant electrical current, electrical current decreases, and for null electrical current in the cooling phase of the actuator, and is then validated by comparison with the experimentally obtained data.


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    Title :

    Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling


    Contributors:
    Grigorie, T L (author) / Botez, R M (author)


    Publication date :

    2009-06-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Adaptive Neuro-Fuzzy Inference Controllers for Smart Material Actuators

    Grigorie, T. / Botez, R. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2010


    Adaptive Neuro-Fuzzy Inference Controllers for Smart Material Actuators

    Grigorie, Lucian / Botez, Ruxandra | AIAA | 2010



    Adaptive neuro-fuzzy controllers for an open-loop morphing wing system

    Grigorie, T L / Botez, R M / Popov, A V | SAGE Publications | 2009