基于粒子群算法的控制系统PID参数优化设计

基于粒子群算法的控制系统

PID 参数优化设计

摘 要

本文主要研究基于粒子群算法控制系统PID 参数优化设计方法以及对PID 控制的改进。PID 参数的寻优方法有很多种，各种方法的都有各自的特点，应按实际的系统特点选择适当的方法。本文采用粒子群算法进行参数优化，主要做了如下工作：其一，选择控制系统的目标函数，本控制系统选用时间乘以误差的绝对值，通过对控制系统的逐步仿真，对结果进行分析。由于选取的这个目标函数的解析式不能直接写出，故采用逐步仿真来实现；其二，本文先采用工程上的整定方法（临界比例度法）粗略的确定其初始的三个参数p K ，i K ，d K ，再利用粒子群算法进行寻优，得到更好的PID 参数；其三，采用SIMULINK 的仿真工具对PID 参数优化系统进行仿真，得出系统的响应曲线。从中发现它的性能指标，都比原来有了很大的改进。因此，采用粒子群算法的优越性是显而易见的。

关键词 目标函数；PID 参数；粒子群算法；优化设计；SIMULINK

Optimal design of PID parameter of the control system based on Particle Swarm Optimization

Abstract

The main purpose of this paper is to study the optimal design of PID parameter of the control system based on Particle Swarm Optimization and find a way to improve the PID control. There are a lot of methods of optimization for the parameters of PID, and each of them has its own characteristics. The proper methods need to be selected according to the actual characteristics of the system. In this paper we adopt the Particle Swarm Optimization to tune the parameters. To finish it, the following tasks should be done. First, select the target function of the control system. The target function of the control system should be chosen as the absolute value of the error multiplied by time. Then we simulate the control system gradually, and analyze the results of the process. Because the solution of the target function cannot be worked out directly, this design adopts simulation gradually. Second, this paper adopts the engineering method (the critical ratio method) to determine its initial parameters p K ,i K ,d K , then uses the Particle Swarm Optimization to get a series better PID parameters. Third, this paper uses the tool of SIMULINK to optimize the parameters of PID and gets the response curve of the system. By contrast with the two response curves, it is clearly that the performance has improved a lot than the former one. Therefore, it is obviously to find the advantages in using the Particle Swarm Optimization.

Key word : target function; PID parameters; Particle Swarm Optimization; optimal design; SIMULINK

目录

摘要 ...................................................................................................... 错误!未定义书签。Abstract................................................................................................... 错误!未定义书签。第1章绪论 ........................................................................................ 错误!未定义书签。

1.1 研究背景和课题意义 ..................................................................... 错误!未定义书签。

1.2 基本的PID参数优化方法 (1)

1.3 常用的整定方法 (2)

1.4 本文的主要工作 (4)

第2章粒子群算法的介绍 ................................................................ 错误!未定义书签。

2.1 粒子群算法思想的起源 (5)

2.2 算法原理 (5)

2.3 算法流程 (6)

2.4 全局模型与局部模型 (7)

2.5 算法特点 (8)

2.6 带惯性权重的粒子群算法 (8)

2.7 粒子群算法的研究现状 (9)

第3章用粒子群方法优化PID参数 (10)

3.1 PID控制原理 (10)

3.2 PID控制的特点 (11)

3.3 优化设计简介 (11)

3.4 目标函数选取 (12)

3.5 大迟滞系统 ..................................................................................... 错误!未定义书签。

3.6加热炉温度控制简介 ..................................................................... 错误!未定义书签。

3.7 加热炉系统的重要特点 ................................................................. 错误!未定义书签。

3.8 加热炉的模型结构 ......................................................................... 错误!未定义书签。第4章系统仿真研究 ........................................................................ 错误!未定义书签。

4.1工程上的参数整定 ......................................................................... 错误!未定义书签。

4.2 粒子群算法参数整定 ..................................................................... 错误!未定义书签。

4.3 结果比较 ......................................................................................... 错误!未定义书签。