Model-Based Control of SMA Actuators with a Recurrent Neural Network in the Shape Control of an Airfoil

Jari Ahola, Tomi Makkonen, Kalervo Nevala, Tomi Lindroos, Pekka Isto

    Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

    2 Citations (Scopus)


    This paper describes the development of a neural network control for shape memory alloy (SMA) actuators as well as the control tests. Precise control of SMA actuators that are integrated in the composite structure is difficult because of the hysteretic behaviour of SMA and time delays in the control system. The weakness of the static model of SMA is that it does not take into account the actuator dynamics. The NARMA-L2 neural network model enables us to solve the inverse dynamic model of the nonlinear discrete-time dynamical system. In this study the NARMA-L2 model of the airfoil (a cross section of wind turbine blade) was created using Matlab Simulink software and neural networks. Data for network training was measured from the specimen. The performance of the constituted NARMA-L2 controller was tested in control tests. According to the control tests the NARMA-L2 controller is well suited for controlling embedded SMA actuators. The constituted controller enabled fast and stable actuation in step responses and it was also able to compensate for changes in cooling conditions.
    Original languageEnglish
    Title of host publicationMechatronic Systems and Materials III
    EditorsZdislaw Gosiewski, Zbigniew Kulesza
    Publication statusPublished - 2009
    MoE publication typeA3 Part of a book or another research book

    Publication series

    SeriesSolid State Phenomena


    • neural network (NN)
    • shape control
    • shape memory alloy
    • system identification


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