[1]张俊玲,陈增强,张青.基于粒子群优化的Elman神经网络无模型控制[J].智能系统学报编辑部,2016,11(1):49-54.[doi:10.11992/tis.201507025]
 ZHANG Junling,CHEN Zengqiang,ZHANG Qing.Elman model-free control method based on particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(1):49-54.[doi:10.11992/tis.201507025]
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基于粒子群优化的Elman神经网络无模型控制(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年1期
页码:
49-54
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
Elman model-free control method based on particle swarm optimization algorithm
作者:
张俊玲1 陈增强12 张青2
1. 南开大学计算机与控制工程学院, 天津 300071;
2. 中国民航大学理学院, 天津 300300
Author(s):
ZHANG Junling1 CHEN Zengqiang12 ZHANG Qing2
1. College of Computer and Control Engineering, Nankai University, Tianjin 300071, China;
2. College of Science, Civil Aviation University of China, Tianjin 300300, China
关键词:
非线性系统非线性离散系统无模型控制控制器Elman神经网络粒子群优化算法
Keywords:
nonlinear systemdiscrete nonlinear systemmodel-free controlcontrollerElman neural networkparticle swarm optimization algorithm
分类号:
TP18;TP301.6
DOI:
10.11992/tis.201507025
摘要:
针对一类无法建模或是建模过程比较复杂的离散SISO非线性离散系统,提出了一种基于Elman神经网络和粒子群优化算法的无模型控制方法。该控制方法是在无需知道被控对象动力学模型的情况下,以Elman神经网络作为控制器结构,利用粒子群优化算法在线学习控制器中的所有权值参数,既而得到每一离散时刻的最优控制量。仿真研究表明,该方法控制下的非线性系统输出信号具有较快的反应速度和较小的跟踪误差,同时控制量信号有较好的收敛性与控制精度,这说明了所提出的基于粒子群的Elman神经网络无模型控制方法是有效与合理的。
Abstract:
In this paper, we propose amodel-free control method, based on the Elman neural network and the particle swarm optimization algorithm, for a class of single-input single-output (SISO) discrete nonlinear systems, whose mathematical model cannot be established or is not easily modeled. In the model-free control system, it is not necessary to establish a mathematical model for each object. The Elman neural network is the controller and all the online weight parameters are learned using the particle swarm optimization algorithm.Using the proposed method, we obtain the optimal control variable at each discrete time.Them odel-free control method simulation results demonstrate that the nonlinear system output signal has a fast response rate and few tracking errors. Moreover, the control variable has good convergence and high control accuracy. These results prove that the proposed method is reasonable and effective.

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备注/Memo

备注/Memo:
收稿日期:2015-07-20;改回日期:。
基金项目:国家自然科学基金资助项目(61174094);天津市自然科学基金资助项目(14JCYBJC18700).
作者简介:张俊玲,女,1990年生,硕士研究生,主要研究方向为无模型控制、智能优化算法;陈增强,男,1964年生,教授,博士生导师,主要研究方向为智能控制、智能信息处理,曾获天津市自然科学二等奖,发表学术论文100余篇;张青,女,1965年生,教授,主要研究方向为复杂系统建模与控制、多智能体系统,发表学术论文30余篇。
通讯作者:陈增强.E-mail:chenzq@nankai.edu.cn.
更新日期/Last Update: 1900-01-01