Fractional-order PID controller optimization via improved electromagnetism-like algorithm
Fractional-order PID controller optimization via improved electromagnetism-like algorithm
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DOI：
10.1016/j.eswa.2010.06.009
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发表时间：
2010-12-01
影响因子：
8.5
通讯作者：
Chang, Fu-Kai
中科院分区：
文献类型：
--
作者：
Lee, Ching-Hung;Chang, Fu-Kai
本文基于类电磁算法（一种进化算法），提出了一种利用遗传算法技术改进的类电磁算法（IEMGA），用于分数阶PID（FOPID）控制器的优化。IEMGA是一种基于种群的元启发式算法，源于电磁理论。它不需要进行梯度计算，并且能够自动收敛到一个良好的解。对于FOPID控制优化，IEMGA通过将每个控制器参数视为电荷来模拟带电粒子的“吸引”和“排斥”。通过使用遗传算法和竞争概念改进了类电磁算法的邻域随机局部搜索。IEMGA在降低类电磁算法的计算复杂度方面兼具类电磁算法和遗传算法的优点。最后，给出了几个示例以展示其性能和有效性。© 2010 Elsevier Ltd.保留所有权利。
Based on the electromagnetism-like algorithm, an evolutionary algorithm, improved EM algorithm with genetic algorithm technique (IEMGA), for optimization of fractional-order PID (FOPID) controller is proposed in this article. IEMGA is a population-based meta-heuristic algorithm originated from the electromagnetism theory. It does not require gradient calculations and can automatically converge at a good solution. For FOPID control optimization, IEMGA simulates the "attraction" and "repulsion" of charged particles by considering each controller parameters as an electrical charge. The neighborhood randomly local search of EM algorithm is improved by using GA and the competitive concept. IEMGA has the advantages of EM and GA in reducing the computation complexity of EM. Finally, several illustration examples are presented to show the performance and effectiveness. (C) 2010 Elsevier Ltd. All rights reserved.