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

机译:通过遗传算法对玻璃窑炉工艺进行PID控制器整定

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摘要

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. © 2015 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
机译:研究了标准遗传算法(SGA),通过三种调整方法,优化了具有回路相互作用的多变量玻璃熔炉工艺的离散时间比例-积分-微分(PID)控制器参数。最初,标准遗传算法(SGA)用于识别植物的控制导向模型,随后将其用于控制​​器优化。首先考虑没有循环交互作用的个体调整方法,以对遗传算子进行分类,成本函数并改善搜索范围,以达到所需的性能标准。第二种调整方法考虑具有回路交互作用和单个成本函数的控制器参数优化。同时,第三种调整方法利用修改后的成本函数,其中包括两个受控变量,玻璃温度和过量氧气的总影响。该修改后的成本函数显示出在回路交互作用下具有改进的控制鲁棒性和干扰抑制性能。 ©2015中国科学院自动化研究所和施普林格出版社柏林海德堡

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