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PID controller tuning for a multivariable glass furnace process by genetic algorithm

  • Kumaran Rajarathinam
  • James Barry GommEmail author
  • Ding-Li Yu
  • Ahmed Saad Abdelhadi
Research Article

Abstract

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.

Keywords

Genetic algorithms control optimisation decentralised control proportional-integral-derivative (PID) control modified cost function multivariable process loop interaction 

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Kumaran Rajarathinam
    • 1
  • James Barry Gomm
    • 1
    Email author
  • Ding-Li Yu
    • 1
  • Ahmed Saad Abdelhadi
    • 1
  1. 1.Mechanical Engineering and Materials Research Centre, Control Systems Group, School of EngineeringLiverpool John Moores UniversityLiverpoolUK

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