基于神经网络的PID网络化控制系统建模与仿真

doi:10.16182/j.issn1004731x.joss.201804026

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (4): 1423-1432.doi: 10.16182/j.issn1004731x.joss.201804026

基于神经网络的PID网络化控制系统建模与仿真

邱占芝^1,2, 李世峰¹

1. 大连交通大学机械工程学院,大连 116028;
2. 大连交通大学软件学院,大连 116028

收稿日期:2016-05-20 修回日期:2016-11-11 出版日期:2018-04-08 发布日期:2019-01-04
作者简介:邱占芝(1960-),女,辽宁朝阳,博士,教授,博导,研究方向为网络控制与智能系统;李世峰(1987-),男,河北唐山,博士生,研究方向为机电系统理论与计算机测控技术。
基金资助:
国家自然科学基金(61074029),大连市计划(2014A11GX006)

Modeling and Simulation of PID Networked Control Systems Based on Neural Network

Qiu Zhanzhi^1,2, Li Shifeng¹

1. Mechanical Engineering Inst, Dalian Jiaotong University, Dalian 116028, China;
2. Software Technology Inst, Dalian Jiaotong University, Dalian 116028, China

Received:2016-05-20 Revised:2016-11-11 Online:2018-04-08 Published:2019-01-04

摘要/Abstract

摘要： 针对网络化控制系统中的时延补偿问题和PID控制器参数整定问题,提出了一类基于改进BP网络的快速PID网络化控制系统。考虑BP网络时延预测模型求取隐含层节点数耗时问题和训练方法的局部最优问题,分别提出了基于计算公式的隐含层节点数求取方法和基于改进遗传算法的BP网络训练方法;考虑PID网络化控制系统中的积分饱和问题、控制器参数整定问题和应对模型失配的能力,设计了基于在线BP网络的PID参数调节器。仿真结果表明：该系统运行速度较快,具有应对模型失配的能力,且在随机短时延和随机长时延情况下均有良好的控制性能。

关键词: 网络化控制系统, 时延预测, PID控制, BP网络, 遗传算法

Abstract: According to the problems of the delay compensation and PID parameters tuning of networked control systems, a class of rapid PID networked control systems based on improved BP network was proposed. Considering the problems of obtaining hidden layer nodes number and local optimum of the BP network delay prediction model, a calculation method was proposed to obtain hidden layer nodes number, and an improved genetic algorithm was proposed to train the BP network. Considering the problems of integral saturation, parameters tuning and model mismatch of the PID network control systems, a PID parameter adjuster was designed based on on-line BP network. The simulation results show that this system has the advantage of rapid running speed, coping model mismatch, and good control performance with random short delay or random long delay.

Key words: networked control systems, delay predication, PID control, BP network, genetic algorithm

中图分类号:

TP391.9

邱占芝, 李世峰. 基于神经网络的PID网络化控制系统建模与仿真[J]. 系统仿真学报, 2018, 30(4): 1423-1432.

Qiu Zhanzhi, Li Shifeng. Modeling and Simulation of PID Networked Control Systems Based on Neural Network[J]. Journal of System Simulation, 2018, 30(4): 1423-1432.

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基于神经网络的PID网络化控制系统建模与仿真

Modeling and Simulation of PID Networked Control Systems Based on Neural Network

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