PSONN在可编程控制系统PID参数优化中的应用  

Application of PSONN in PID Parameter Optimizing of the Programmable Logic Controller System

作　　者：陆岚[1] 杨加国[2] 

机构地区：[1]四川工商职业技术学院,四川成都611837 [2]成都大学,四川成都610106

出　　处：《计算机仿真》2012年第5期227-230,共4页Computer Simulation

摘　　要：研究可编程控制系统优化问题,可编程控制系统具有非线性、时变性等特点,传统PID控制器优化方法难以建立精确的数学模型,使得系统参数设定困难,导致可编程控制系统的控制效果不理想。为了解决传统的PID算法所带来的问题,利用RBF神经网络非线性、自学习能力,提出一种基于粒子群神经网络的PID参数优化算法。将粒子群和神经网络相结合,形成了一种智能控制算法,并将应用于可编程控制系统。测试结果表明,粒子群神经网络提高了PID控制参数优化速度,提高了可编程控制系统可靠性和鲁棒性,具有一定的理论和实用价值。The modern industry control systems are of the characteristics of nonlinear and time-varying,and the it is difficult using traditional PID controller parameter optimization method to establish the precise mathematical model for the PLC control system,and the control effects are not ideal.The using the nonlinear and self-learning ability of RBF neural network,the paper proposed a PID parameter optimization algorithm based on particle swarm neural network,and applied it in PLC control system.The test results show that particle swarm neural network can improve the PID control parameter optimization speed,and obtain the global optimal PID parameters,thus improve the reliability of PLC control system and robustness,and has certain theoretical and practical value.

关 键 词：控制系统 可编程逻辑控制器 神经网络 粒子群神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]