摘要在这次论文研究中，我们基于 PID 控制的混沌同步，提出了一个粒子群优化（PSO）算法的改进方法，我们把这种方法称之改进粒子群优化（IPSO）算法。 它更新完成后采用全局最佳位置来执行这样的全局搜索策略，另外，IPSO 引入 了全局最优解。通过结合变异操作和全局搜索策略，IPSO 算法的全局搜索能力 变的更加的强大了，它可以很轻易地摆脱局部最优。IPSO 算法主要在三个方面 进行了改进，使得算法更加的适应了各个领域的应用。69633

最一开始提出粒子群优化算法（PSO）的是 Kennedy 和 Eberhart 在 1995 年 提出的。算法的优点是简单易读性和适用性。因为它的成功研发，已经成功地解 决了大量的问题。例如时间序列预测和电力系统最优潮流问题、电力系统稳定设 计优化问题、变量强度均匀覆盖最好的阵列结构和网络安全等。

该论文有图 17 副，表 5 个，参考文献 22 篇，还有 1 组源代码。

毕业论文关键词：改进粒子群优化 混沌同步 PID 控制 全球搜索 突变

An Improved Particle Swarm Optimization Algorithm For Chaotic Synchronization Based On PID Control

Abstract

In this study, a new version of particle swarm optimization (PSO) algorithm is proposed for coping with chaotic synchronization based on PID control.  This approach is called improved PSO, and it utilizes the global best position to carry out a global searching process such that the position updating is successfully executed. Furthermore, the IPSO adopts a mutation operation according to the personal best positions. Based on the global searching and mutation operation, the global searching capacity of the IPSO is improved, and it can easily escape from the local optima. The performance of the IPSO is tested on four instances and its results are compared with those of the PSO. The experimental analysis verifies the efficiency of IPSO.

At the beginning,the particle swarm optimization algorithm (PSO) was proposed by Kennedy and Eberhart in 1995.PSO has the advantages of easy readability and applicability.Because of its success in research, a large number of problems have been successfully solved,for example time series prediction and power system optimal power flow problem, power system stabilize design optimization problem,variable strength evenly covering the best array structure and network security etc.

This paper has 17 figures, 5 tables,22 references,there are 1 groups of source code.

Key words:Improved Particle Swarm Optimization Chaotic Synchronization PID Control Global Searching Mutation

目 录

摘要-Ⅰ

Abstract--Ⅱ

目录 III

图清单 V

表清单 V

1 PID 的工业背景 1

1.1 PID 控制法的应用 2

1.2 国内外实例 3

1.3 智能 PID 控制 5

1.4 PID 控制的前景 6

2 PID 控制用来实现混沌同步 8

2.1 混沌同步的简介 8

2.2 PID 对混沌系统的控制 8

2.3 PID 控制的非线性时滞离散混沌系统 10

2.4 PID 控制混沌同步的前景