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PID Controller Tuning for a Multivariable Glass Furnace Process by Genetic Algorithm 被引量:4
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作者 Kumaran Rajarathinam James Barry Gomm +1 位作者 Ding-Li Yu Ahmed Saad Abdelhadi 《International Journal of Automation and computing》 EI CSCD 2016年第1期64-72,共9页
Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) con- troller parameters, by three tuning approaches, for a multivariable glass furnace process w... Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) con- troller 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 optimisa- tion. 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. 展开更多
关键词 Genetic algorithms control optimisation decentralised control proportional-integral-derivative (PID) control modifiedcost function multivariable process loop interaction.
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