Pneumatic cylinders are used in many industrial applications to position loads using a rectilinear motion. Currently, pneumatic cylinders are limited to a narrow range of applications because their motion trajectory is difficult to control. Conventional linear control methods can not compensate for both the nonlinear flow of compressed air and the internal friction present in the cylinders. Multilayer neural networks (MNNs) are nonlinear mappings which can be used to compensate for the nonlinear nature of these dynamic systems. A model of a pneumatic cylinder was developed to provide training data for a MNN. The MNN was designed to cancel the cylinder dynamics and was implemented as a feedforward controller in conjunction with a PID feedback controller. The MNN was trained over a range of constant velocity trajectories. The resultant controller allows the model to track the constant velocity training trajectories as well as trajectories for which the MNN was not trained.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pneumatic cylinder trajectory tracking control using a feedforward multilayer neural network


    Contributors:
    Gross, D.C. (author) / Rattan, K.S. (author)


    Publication date :

    1997-01-01


    Size :

    725815 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pneumatic Cylinder Trajectory Tracking Control Using a Feedforward Multi-Layer Neural Network

    Gross, D. / Rattan, K. / IEEE | British Library Conference Proceedings | 1997



    A Homotopy Recursive Algorithm for Multilayer Feedforward Neural Network

    Yang, D. / Liu, Z. / Zhou, Z. et al. | British Library Conference Proceedings | 1994


    Trajectory tracking for vehicle lateral control using neural network

    PENG FARUI / LITKOUHI BAKHTIAR B | European Patent Office | 2020

    Free access

    TRAJECTORY TRACKING FOR VEHICLE LATERAL CONTROL USING NEURAL NETWORK

    PENG FARUI / LITKOUHI BAKHTIAR B | European Patent Office | 2019

    Free access