基于DSP的模糊神经网络控制算法在电阻炉上的应用  

Application of DSP-Based Fuzzy Neural Network Algorithm to Electric Resistance Stove

作　　者：李界家[1] 武彧[1] 孙卫娜[2] 刘喜梅 

机构地区：[1]沈阳建筑大学信息与控制工程学院,辽宁沈阳110168 [2]沈阳航空发动机研究所,辽宁沈阳110015 [3]朝阳东风柴油机配件有限责任公司,辽宁朝阳122000

出　　处：《沈阳建筑大学学报（自然科学版）》2008年第2期337-339,共3页Journal of Shenyang Jianzhu University：Natural Science

基　　金：辽宁省教育厅基金项目(20060699)

摘　　要：目的使电阻炉控制技术适应非线性、大滞后、大惯性、时变对象的控制要求,提高炉温的热效率和控制效果,达到节能的目的.方法通过对电阻炉工作特性分析,根据电阻炉的特点及控制指标要求,采用了模糊控制与神经网络技术相结合方法,应用了DSP芯片,构建了基于DSP技术硬件和软件技术平台.结果采用模糊控制神经网络控制方法,提高了系统的稳态性能和动态性能,减小超调量,缩短了动态调节时间,提高了控制系统的实时性,有效地实现了对电阻炉的温度控制.结论基于DSP的模糊神经网络控制算法优于常规的控制方法,使炉温的控制精度大大提高,抗干扰能力增强,取得了良好的控制效果.In order to improve thermal effidency and control effect of the furnace temperature, we adapt resistance furnace control technology to the control requirements, such as nonlinear, large delay, the inertia and time - varying. According to the characteristics of resistance furnace and control target, adopting fuzzy control with neural network technology, using the P.GP chips, we constructed hardware and software technological platform based on P.GP technology. The proposed technology can improve the system＇ s steady state and dynamic performances, reduce overshoot, shorten the dynamic regulating time. It improves the real time of the control system, realizes the temperature control of the resistance furnace effectively. DSP - based fuzzy neural network control algorithm is better than conventional control methods in that it greatly improves temperature control accuracy, enhances anti - jamming capability and achieves good control resuits.

关 键 词：DSP 温度 电阻炉 模糊神经网络控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]