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Machine Intelligence Research

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01.02.2016 | Research Article

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

verfasst von: Kumaran Rajarathinam, James Barry Gomm, Ding-Li Yu, Ahmed Saad Abdelhadi

Erschienen in: Machine Intelligence Research | Ausgabe 1/2016

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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.

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Titel: PID controller tuning for a multivariable glass furnace process by genetic algorithm
verfasst von: Kumaran Rajarathinam
James Barry Gomm
Ding-Li Yu
Ahmed Saad Abdelhadi
Publikationsdatum: 01.02.2016
Verlag: Institute of Automation, Chinese Academy of Sciences
Erschienen in: Machine Intelligence Research / Ausgabe 1/2016
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI: https://doi.org/10.1007/s11633-015-0910-1

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