基于随机搜索算法的PID控制器整定

Dung Vuong Quoc, Thuy Tran Thi, N. D. Minh, Minh Nguyen Quang, Loi Nguyen Tien, Binh Nguyen Thi Thanh
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引用次数: 0

摘要

本文提出了遗传算法(GA)和粒子群算法(PSO)这两种常用的随机搜索算法,用于寻找作为对象的直流电动机模型的PID控制器的最优参数。为了测试遗传算法和粒子群算法的性能,我们将它们与传统的Ziegler-Nichols算法在性能指标方面进行了比较。仿真结果表明,采用遗传算法和粒子群算法设计的比例积分导数控制器(PID)在性能指标上优于传统方法。Keywords-Tuning PID;GA算法;PSO算法;Ziegler-Nichols方法;性能指标;优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tuning PID Controller Bases on Random Search Algorithms
In this paper, we present two well known random search algorithms which are Genetic algorithm (GA) and Particle Swarm optimization (PSO) to find optimal parameters for PID controller for a model of DC motor is used as a plant. To test performance of GA and PSO algorithms, we compare them with the traditional Ziegler-Nichols method in term of performance indices. The simulation results show that Proportional Integral and Derivative controller (PID) designed by GA and PSO algorithms yields better results than the traditional method in terms of the performance index. Keywords—Tuning PID; GA algorithm; PSO algorithm; Ziegler-Nichols method; performance index; optimization.
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