首页 | 本学科首页   官方微博 | 高级检索  
     

大滞后网络拥塞的智能前馈组合控制系统
引用本文:宋泽云,吴荣腾. 大滞后网络拥塞的智能前馈组合控制系统[J]. 贵州工业大学学报(自然科学版), 2004, 33(5): 99-102
作者姓名:宋泽云  吴荣腾
作者单位:1. 福州大学机械工程学院,福建,福州,350002
2. 福州大学数学与计算机学院,福建,福州,350002
摘    要:在基于速率反馈的网络流量控制系统中,信元在网络中的传播时延,特别是网络流量控制中的非线性大滞后,会带来极大的网络拥塞和数据丢失。针对流量控制系统中存在的非线性大滞后和不确定性,提出了一种新的基于智能前馈控制策略的组合控制器。前馈控制部分由两个神经网络分别实现逆模型辩识与直接逆控制,可以在线调整网络权值;基本控制器由PID和Fuzzy PID实现分段控制,根据不同误差变化范围调整控制组合。仿真表明本方案能使信元发送速率快速响应网络变化,特别对于大滞后对象,控制的适应性和鲁棒性更好。

关 键 词:信元 流量控制 滞后 逆模型辩识 神经网络
文章编号:1009-0193(2004)05-0099-04

Intelligent Feed-forward Combination Control System for Nonlinear Large-lag Congestion in Networks
SONG Ze-yun,WU Rong-tengCollege of Mechanical Engineering and Automation,Fuzhou University,Fuzhou ,China, College of Mathematics and Computer Science,Fuzhou University,Fuzhou ,China. Intelligent Feed-forward Combination Control System for Nonlinear Large-lag Congestion in Networks[J]. Journal of Guizhou University of Technology(Natural Science Edition), 2004, 33(5): 99-102
Authors:SONG Ze-yun  WU Rong-tengCollege of Mechanical Engineering  Automation  Fuzhou University  Fuzhou   China   College of Mathematics  Computer Science  Fuzhou University  Fuzhou   China
Affiliation:SONG Ze-yun,WU Rong-tengCollege of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350002,China, College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350002,China
Abstract:The propagation delay of cells, especially the nonlinear large-lag of communication may create congestion and loss of data in rate-based flow control in networks. Proposed in this paper is a kind of feed-forward combination controller, which can better overcome the adverse effect caused by the large time delay and its indeterminacy. The feed-forward control part consists of two neural networks,one realizes identification of inverse model,and the other is used for inverse control. The basic control part is made up of PID and fuzzy PID controller which can self-switch between them according to various stages of output error so as to adjust combination of flow control in network. The simulation shows that the scheme can make source rates respond to the changes of network status rapidly, avoid the congestion. It has much better adaptability and robustness in large time delay control system.
Keywords:cell  flow control  lag  identification of inverse model  neural network
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号