基于智能方法的真空退火炉建模与控制

Modeling and Control for Vacuum Annealing Furnace Based on Intelligent Method

真空退火炉中工件温度的精确控制是一个具有非线性和不确定性的复杂控制问题.为了实现工件温度的精确控制,以现场实际采集的数据为基础,采用小波神经网络建立对象的模型,利用自适应免疫遗传算法对小波神经网络的权值、小波基的个数和伸缩、平移因子等进行优化,提出了一种精确控制真空退火炉工件温度的优化数学模型,仿真与实验研究表明,用此方法建立的模型,其控制效果优于BP神经网络所建立模型的控制;同时,加快了网络训练速度,提高了系统的稳态精度,使系统具有较强的实时性和鲁棒性.

The accurate control of the temperature of work pieces is a nonlinear uncertain complicated control problem in vacuum annealing furnace. In order to control the work piece temperature accurately, the optimization model for accurately controlling work piece temperature is proposed by the data gathered from the scene. The model is set up based on wavelet neural networks, and an adaptive immune genetic algorithm is used to optimize the number of elements in hidden layer, weights, dilation and translation parameters of wavelet neural networks. Simulation and experiment results show that the model based on the method in this paper is better than the model based on BP neural networks. And, it improves the training rate of networks and steady state precision of system, and obtains a system that has good characteristics of real time and robustness.