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

作     者：Kumaran Rajarathinam James Barry Gomm Ding-Li Yu Ahmed Saad Abdelhadi 

作者机构：Mechanical Engineering and Materials Research Centre Control Systems Group School of EngineeringLiverpool John Moores University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2016年第13卷第1期

页      面：64-72页

核心收录：

学科分类：080706[工学-化工过程机械] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：Genetic algorithms control optimisation decentralised control proportional-integral-derivative (PID) control modifiedcost function multivariable process loop interaction. 

摘      要：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.