Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust wavelet neuro control for linear brushless motors


    Contributors:
    Lin, C.L. (author) / Shieh, N.C. (author) / Tung, P.C. (author)


    Publication date :

    2002-07-01


    Size :

    714483 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Brushless Dc Motors Failure Detection Using The Continuous Wavelet Transform

    Fico, V. M. / Martin Prats, M. A. / Rodríguez Vazquez, A. L. | Online Contents | 2014


    Brushless Dc Motors Failure Detection Using The Continuous Wavelet Transform

    Fico, V. M. / Martin Prats, M. A. / Rodríguez Vazquez, A. L. | Springer Verlag | 2014