摘要
减压塔侧线温度系统是一个时变非线性复杂系统,采用常规的PID控制回路难以达到较好的控制品质。针对克拉玛依石化厂原油蒸馏装置中的减压塔,根据实际控制要求,提出了RBF神经网络模型参考自适应控制策略,设计了减压塔减三线温度控制系统,给出了RBF神经网络控制器和模型辨识网络参数的学习算法。仿真结果表明,采用提出的控制策略,控制效果非常好,完全达到控制要求。
Side line temperature system of vacuum tower is a time-varying,non-linear,complex system,so it is difficult to meet the control request with the traditional PID control method.Aiming at the vacuum tower in the crude oil distillation unit of Karamay petrifaction factory,RBF neural network model reference adaptive control strategy was introduced;No.3 side line temperature control system of vacuum tower was designed and learning algorithms of RBF neural network controller and parameters of model identification network were given.From simulation result,it receives good effect and meets the requirement of control.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2008年第2期429-432,共4页
Journal of System Simulation
关键词
减压塔
RBF神经网络
模型参考自适应控制
模型辨识
vacuum tower
RBF neural network
model reference adaptive control
model identification
