Kumaran Rajarathinam, James Barry Gomm, Ding-Li Yu and Ahmed Saad Abdelhadi. PID Controller Tuning for a Multivariable Glass Furnace Process by Genetic Algorithm. International Journal of Automation and Computing, vol. 13, no. 1, pp. 64-72, 2016. DOI: 10.1007/s11633-015-0910-1
Citation: Kumaran Rajarathinam, James Barry Gomm, Ding-Li Yu and Ahmed Saad Abdelhadi. PID Controller Tuning for a Multivariable Glass Furnace Process by Genetic Algorithm. International Journal of Automation and Computing, vol. 13, no. 1, pp. 64-72, 2016. DOI: 10.1007/s11633-015-0910-1

PID Controller Tuning for a Multivariable Glass Furnace Process by Genetic Algorithm

  • Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return