基于RBF神经网络自适应PID的预焙阳极焙烧炉温度控制  

Adaptive PID Control Based on RBFNN for Temperature System of Anode Pre-baking Furnace

作　　者：张明光[1] 王兆刚[1] 王鹏[1] 

机构地区：[1]兰州理工大学电气工程与信息工程学院,甘肃兰州730050

出　　处：《工业加热》2008年第1期16-19,共4页Industrial Heating

基　　金：国家高技术研究发展计划(863计划)(2002BA901A28);甘肃省省长基金项目(GS015-A52-012)

摘　　要：针对工况变化频繁的焙烧炉焙烧过程,提出了一种基于径向基函数(RBF)神经网络自适应PID的控制策略,该方法是通过神经网络的自学习能力在线调整PID控制器的参数,因而,其兼顾神经网络和传统PID控制的特点,能根据被控对象当前特征迅速地做出相应决策、克服实际控制过程稳态性和准确性之间的矛盾。将其应用于预焙阳极焙烧炉温度过程控制中,实验结果表明:它具有很强的自适应能力和鲁棒性,达到了满意的控制效果。An adaptive PID control strategy based on Radial Basis Function （RBF） neural network （NN） is presented for the baking process of the furnace, whose operating conditions change frequently, and the parameters of PID controller are tuned on-line using the self-learning ability of RBFNN. So the proposed control strategy has the advantages of neural network and conventional PID control, and the ability of making correspondingly the decisions quickly according to the current characteristic of the object and overcoming the inconsistency of the steady and veracity. It was applied to the temperature system of the anode pre-baking furnace, the results show that the proposed controller has the adaptability, strong robustness and satisfactory control performance.

关 键 词：预焙阳极焙烧炉 RBF神经网络 自适应PID控制 温度控制 

分 类 号：TF06[冶金工程—冶金物理化学] TP273[自动化与计算机技术—检测技术与自动化装置]