摘要
基于BP神经网络整定参数的PID控制算法有着广泛的应用,然而现有的研究主要针对控制器和被控对象在同一位置的点对点系统。实际中网络控制系统被广泛采用,由于通信网络的存在使得控制系统的数据经常发生丢失现象,对控制性能造成很大影响。针对上述问题,将数据丢包描述为一个随机的伯努利序列,在此基础上给出了存在数据丢包的神经网络PID控制算法。仿真结果表明,当控制系统存在一定的数据丢失时,神经网络PID控制算法仍然可以保证系统的稳定性,但输出性能随着数据丢失程度的增加变差。
The BP neural network based PID control algorithms have been widely applied in many systems. However, the existing applications only consider the point-to-point control systems, and the controllers and plants are assumed at same position. In practical systems, networked control systems are widely introduced. Due to the failure of communication channel, data dropouts often occur in networked control systems, which results in worse output per- formance. This paper described the dropout rate as Bernoulli random variables, and then the BP neural network based PID control was given in the framework of data dropouts. Simulation results show that the control algorithm can guarantee the stability of systems even though some data are missing, and the output performance becomes worse as the increasing of data dropout rate.
出处
《计算机仿真》
CSCD
北大核心
2014年第5期419-422,431,共5页
Computer Simulation
基金
河南省科技攻关计划项目资助(112102210004)
作者简介
付子义（1958－），男（汉族），河南博爱人，教授，硕士研究生导师，研究方向为煤矿井下供配电系统。
张艺（1987－），女（汉族），河南南阳市人，硕士研究生，研究方向为计算机网络技术。
