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Design Optimization of PID Controller in Automatic Voltage Regulator System Using Taguchi Combined Genetic Algorithm Method

机译:基于Taguchi组合遗传算法的自动调压系统PID控制器设计优化。

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The optimum design of the proportional-integral-derivative (PID) controller plays an important role in achieving a satisfactory response in the automatic voltage regulator (AVR) system. This paper presents a novel optimal design of the PID controller in the AVR system by using the Taguchi combined genetic algorithm (TCGA) method. A multiobjective design optimization is introduced to minimize the maximum percentage overshoot, the rise time, the settling time, and the steady-state error of the terminal voltage of the synchronous generator. The proportional gain, the integral gain, the derivative gain, and the saturation limit define the search space for the optimization problem. The approximate optimum values of the design variables are determined by the Taguchi method using analysis of means. Analysis of variance is used to select the two most influential design variables. A multiobjective GA is used to obtain the accurate optimum values of these two variables. MATLAB toolboxes are used for this paper. The effectiveness of the proposed method is then compared with that of the earlier GA method and the particle swarm optimization method. With this proposed TCGA method, the step response of the AVR system can be improved.
机译:比例积分微分(PID)控制器的优化设计在自动电压调节器(AVR)系统中获得令人满意的响应方面起着重要作用。利用田口组合遗传算法(TCGA),提出了AVR系统中PID控制器的一种新型优化设计。引入了多目标设计优化,以最大程度地减小最大百分比过冲,上升时间,稳定时间以及同步发电机端电压的稳态误差。比例增益,积分增益,微分增益和饱和极限定义了优化问题的搜索空间。设计变量的近似最佳值是通过田口方法使用均值分析法确定的。方差分析用于选择两个最具影响力的设计变量。多目标遗传算法用于获得这两个变量的准确最佳值。本文使用了MATLAB工具箱。然后将所提方法的有效性与早期遗传算法和粒子群优化方法的有效性进行比较。利用该提出的TCGA方法,可以改善AVR系统的阶跃响应。

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