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群体智能优化算法及其在控制器参数整定中的应用
Alternative TitleSwarm intelligence optimization algorithms and the application in the tuning of controllers
韩久琦
Subtype工程硕士
Thesis Advisor杨鑫
2013-05-23
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机技术
Keyword群体智能优化算法 果蝇优化算法 粒子群算法 Pid控制器 Swarm Intelligence Optimization Algorithms Fruit Fly Optimization Algorithm Particle Swarm Optimization Pid Controllers
Abstract最优化是人类从古至今一直在面对的问题,在自然科学和社会科学中都广泛存在。为了解决最优化问题,不断地有各种各样的优化算法出现,群体智能优化算法是最新的优化算法,它出现于上世纪80年代,随着蚁群算法和粒子群算法的不断发展和一系列成功应用而逐渐步入人们的视线。进入本世纪以来,许多新的群体智能优化算法层出不穷,例如细菌觅食算法、人工鱼群算法等。同时,经典的粒子群算法仍然得到了广泛的关注,在优化问题中发挥了巨大的作用。 虽然群体智能优化算法已经取得了令人瞩目的进步和巨大的成就,但是目前其搜索性能仍无法满足人们越来越高的要求,群体智能优化算法最大的优势在于其涌现特性,而在提高个体的学习能力和群体内部交流机制方面还有许多改进的空间,本文即是以此为出发点,针对两个基本的算法提出三项改进措施。 PID控制因其结构简单、调试方便、鲁棒性好、硬件易于实现等优点,逐渐成为了现代工业控制中应用最广泛的控制器。因为PID控制器的性能完全取决于其参数,所以自从其诞生的时候起,其参数的整定就成为了备受关注的问题之一。近些年来,随着科学技术的不断发展,传统整定方法的缺点也逐渐暴露出来,这就出现了许多基于优化算法的整定方法,而基于群体智能优化算法的PID控制器参数整定是其最新的成果,具有广阔的前景。 本文的主要工作和创新点有: 1. 提出了一种双中心的进化策略,群体不再以一个位置为目标进行搜索,并将其应用于果蝇优化算法。 2. 提出了一种带竞争和维度交叉的粒子群算法,个体的搜索方向取决于目标值优而且距离当前个体近的个体,同时在每一代中将不同个体的不同维度进行随机交叉,这样可以避免群体过早陷入局部极小值点,同时可以使未收敛的维度收敛。 3. 将双中心果蝇优化算法、带竞争的粒子群算法和维度交叉粒子群算法分别应用于PID控制器的参数整定,实验结果证明了其有效性。
Other AbstractThroughout history, people have to confront the optimization problems, which exist widely in both the natural science and the social science. Myriad optimization algorithms have been proposed to solve the problems, and the swarm intelligence optimization algorithms are the latest, which were born in the 80s last century. They became famous accompanying the continuous development and successful application of the ant colony algorithm and the particle swarm optimization. Many original swarm intelligence optimization algorithms, such as bacteria foraging optimization algorithm and artificial fish swarm algorithm, have been proposed from the beginning of this century. At the same time, classical PSO is paid much attention and plays an important role in the optimization problems. Although swarm intelligence optimization algorithms have made marvelous achievement, their performance is still far away from the increasing demands. The feature of emergence is a great advantage of swarm intelligence optimization algorithms, therefore we should strengthen the learning ability of the particles and enhance the communication in the swarm. This thesis aims at these two parts, proposing three improved measures on two basic algorithms. PID controllers are the most widespread controllers in the modern industrial control by virtue of their several priorities such as the simplicity of structure, the convenience of debug, the robustness and the ease of hardware implementation. Because the performance of the PID controllers depends completely on the parameters, the tuning of parameters has been the core problem since PID controllers were proposed. Recent years, with the technology progressing rapidly, the traditional tuning methods expose much weakness, followed by many tuning methods based on optimization algorithms. The method based on swarm intelligence optimization algorithms is the up-to-date tuning method, and it has relatively broad prospects. The major contributions of the thesis are: Firstly, the strategy of double centers is put forward, in which the swarm is not searching toward the same point any more, and is applied to the fruit fly optimization algorithm. Secondly, the searching type of battle and the operator of cross on dimensions are presented. With the battle, the searching target of a particle is not only depends on the fitness, but also the distance, so that the swarm is not prone to fall into the local minima too early. Furthe...
shelfnumXWLW1937
Other Identifier2010E8014669015
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7686
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
韩久琦. 群体智能优化算法及其在控制器参数整定中的应用[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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