基于模糊隶属函数优化的炉内温控数学模型

Temperature Control Mathematical Model in Furnace Based on Fuzzy Membership Function Optimization

研究炉内温度的鲁棒性控制方法。利用传统模型进行炉内温度鲁棒性控制,需要建立炉温多变量时间序列模型,从而实现炉内温度的鲁棒性控制。但是由于炉内状况多变,不确定性很强,而且炉温多变量控制模型的输入信息量过少,无法准确描述该模型中多个变量之间的关系,控制效果不佳。提出了一种基于模糊隶属函数优化的炉内温控数学模型。利用传感器采集炉内状态信号,并对其进行滤波处理,添加模糊隶属度控制函数,建立模糊控制数学模型,从而实现炉内温度的鲁棒性控制。实验结果表明,利用该模型进行炉内温度的鲁棒性控制,能够极大地提高控制的准确性。

The robustness of furnace temperature control method was researched. The traditional model needed to establish the furnace temperature multivariate time series model for realizing the robustness control. Because the condition was complicated and uncertainty in furnace, and the input information of control model was too less, the relationship between multiple variables could not be described precisely, the control effect was not good. An optimal tem- perature control mathematical model in furnace based on fuzzy membership function optimization was proposed. The status signal in furnace was collected with sensors, the signal was filtered, and the fuzzy membership function was added, the fuzzy control mathematical model was established, and the robustness control was realized. Simulation re- suits show that the control precision can be improved greatly with new method.