基于改进粒子群算法在加热炉温度控制中的应用  

Application of Improved Particle Swarm Optimization Algorithm in Temperature Control of Heating Furnace

作　　者：张志响 张靖伟 王炳奎 

机构地区：[1]宝武集团鄂城钢铁炼铁厂,湖北鄂州436000

出　　处：《工业控制计算机》2020年第5期119-121,共3页Industrial Control Computer

摘　　要：加热炉温度具有强非线性、多时变以及大滞后等特点,常规PID控制很难达到良好的控制效果,采用了一种改进型粒子群算法(IPSO)来优化PID参数,再结合CMAC(小脑模型关联控制器)共同对加热炉温度进行控制。通过对实际加热炉温度系统的仿真表明,该算法具有良好的控制效果。The temperature of heating furnace has the characteristics of strong non-linearity,multi time variation and large lag,so it is difficult to achieve good control effect by conventional PID control.In this paper,an improved particle swarm optimization algorithm(IPSO)is used to optimize the parameters,and then combined with the cerebellar model associated controller to control the temperature of heating furnace.The simulation of the actual temperature system of heating furnace shows that the algorithm has good control effect system effect.

关 键 词：加热炉 神经网络 粒子群算法 CMAC PID 

分 类 号：TG307[金属学及工艺—金属压力加工] TP18[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]