MURAL - Maynooth University Research Archive Library



    Control of pH in-line using a neural predictive strategy


    Gomm, J.B. and Doherty, Sean and Williams, D. (1996) Control of pH in-line using a neural predictive strategy. In: UKACC International Conference on Control '96 (Conf. Publ. No. 427). IET, pp. 1058-1063. ISBN 0852966687

    [img]
    Preview
    Download (551kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Control of an experimental in-line pH process exhibiting varying nonlinearity and deadtime is described. A radial basis function (RBF) artificial neural network is used to model the nonlinear dynamics of the process. Accommodation of the varying process deadtime in the neural model is achieved by the generation of a feed-forward signal, for input to the neural network, from a downstream pH measurement. The feedforward signal is derived from a variable delay model based on process knowledge and a flow measurement. The neural model is then used to realise a predictive control scheme for the process. Development of the neural process model is described and results are presented to illustrate the performance of the neural predictive control scheme which is tested as a regulator at different setpoints.

    Item Type: Book Section
    Keywords: pH control; process control; predictive control; neurocontrollers; nonlinear control systems; feedforward neural nets; delays; control system synthesis;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 9685
    Identification Number: https://doi.org/10.1049/cp:19960699
    Depositing User: Seán Doherty
    Date Deposited: 17 Jul 2018 16:10
    Publisher: IET
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year