Self-tuning Fractional Order PID Based on Hybrid PSO Neural Networks

HU Hai-bo; HUANG You-rui

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Self-tuning Fractional Order PID Based on Hybrid PSO Neural Networks

更新时间：2026-09-04

- Self-tuning Fractional Order PID Based on Hybrid PSO Neural Networks
- Vol. 27, Issue 5, Pages: 157-161(2010)
- 作者机构：
  
  安徽理工大学机构电气与信息工程学院,安徽,淮南,232001
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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胡海波, 黄友锐. 基于混合PSO神经网络的自整定分数阶PID控制器[J]. 微电子学与计算机, 2010, 27(5): 157-161,166.

HU Hai-bo, HUANG You-rui. Self-tuning Fractional Order PID Based on Hybrid PSO Neural Networks[J]. 2010, 27(5): 157-161.
胡海波, 黄友锐. 基于混合PSO神经网络的自整定分数阶PID控制器[J]. 微电子学与计算机, 2010, 27(5): 157-161,166. DOI：

HU Hai-bo, HUANG You-rui. Self-tuning Fractional Order PID Based on Hybrid PSO Neural Networks[J]. 2010, 27(5): 157-161. DOI：

摘要

提出了一种基于混合PSO和RBF神经网络的自整定分数阶PID控制器的设计方法.该控制器主要由三个部分组成:(1)分数阶PID控制器直接控制被控对象;(2)利用细菌觅食算法和粒子群算法混合优化分数阶PID参数值

作为初始值;(3)利用RBF神经网络具有以任意精度逼近非线性函数及训练速度快的优点

在线整定分数阶PID值

并完成对被控对象的Jacobian信息辨识.实验仿真结果表明:该控制器具有响应速度快、收敛精度高、鲁棒性强等特点

可适用于不同的对象和过程

特别是复杂的、无确定数学模型的控制系统.

Abstract

A self-tuning fractional order PID controller based on hybrid PSO and neural networks is presented. It consists of three parts. In the first part

a fractional order PID controller directly controls the object. In the second part

a group of fractional order PID parameters are obtained by hybrid PSO. The third part

RBF neural networks is used to optimize and adjust the fractional order PID parameters on-line by exploiting the nonlinear mapping capabilities of neural networks and training fast and get Jacobian information for the object. The simulation results show that the controller has a fast response speed

high convergence and robustness. It can be used to control different objects and processes

specially complex

non-determination mathematical model control system.

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Related Institution

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