模糊神经网络在冷连轧厚度控制中的应用

由于冷连轧厚度控制系统具有非线性、大时滞的特点,在冷连轧厚度的常规 PID 控制中,PID 控制器的参数往往针对某一种情况进行整定,很难控制冷连轧厚度始终处于一个好的状态.为此,在分析了厚度控制原理的基础上,设计了用一个2-5-1结构的 BP 网络实现的模糊神经网络控制器(FNNC),并将该 FNNC 控制器与积分作用相结合构成一个 FNNC-I 控制器.仿真结果表明,该 FNNC-I 控制器提高了系统的动态和稳态性能、抗干扰性以及鲁棒性,其控制效果优于常规 PID 控制器.

Fuzzy Neural Network on the Application in Thickness Control of Tandem Cold Mill

There are nonlinear,large time delay characteristics of tandem cold mill thickness control,so it is difficult to keep thickness within a small tolerance using PID controller,whose parameters are set only for one stable situation.Based on the analysis of thickness control theory,a fuzzy neural network controller(FNNC)with simple structure was designed,which was realized by a BP network with 2-5-1 structure.On the basis of this controller,an intergral action was added to constitute FNNC-I controller.Simulation results show that the dynamic,static,anti-interference performance and the robusness of the system were all improved by this FNNC-I controller,so it is better than the conventional PID controller.