基于改进蚁群算法的加热炉温度控制研究  

Research on temperature control of heating furnace based on improved ant colony algorithm

作　　者：周建新 黄剑雄 Jian-xin ZHOU;Jian-xiong HUANG(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063000,China)

机构地区：[1]华北理工大学电气工程学院,河北唐山063400

出　　处：《机床与液压》2020年第18期157-162,195,共7页Machine Tool & Hydraulics

基　　金：河北省教育厅重点项目(ZD2618257)。

摘　　要：加热炉温度控制系统具有非线性、时变性、滞后性等缺陷,导致系统控制过程中响应速度慢、抗干扰能力差,传统的控制方法无法对其进行精确控制。将免疫算法引入蚁群算法中,根据免疫算法中的亲和力原理,增加蚁群的多样性,并对蚁群的初始信息素规则进行改进。利用改进的蚁群算法来调节PIT神经网络(PIDNN)的权值,提出一种新型PITNN控制方法,并采用计算机仿真软件对其进行实验。仿真实验结果表明,与传统的PITNN控制方法相比,当采用改进蚁群算法的PIDNN控制器对加热炉进行控制时,系统达到稳态所需时间缩短了约34%;当加入扰动后,系统恢复到稳态所需时间减少了约26%,振动幅度明显降低,加热炉控制系统的抗干扰能力增强。The temperature control system of reheating furnace has the defects of nonlinearity,time-varying ad hysteresis,which results in slow response speed and poor anti-interference ability in the process of system control.The traditional control method cannot control it accurately.The immune algorithm was introduced into the ant colony algorithm.According to the afinity principle of the immune algorithm,the diversity of the ant colony was increased,and the initial pheromone rule of the ant colony was improved.The improved ant colony algorithm was used to adjust the weight of PIDneural network(PIDNN),and a new PIDNNcontrol method was proposed.The simulation results show that,compared with the traditional PIDNN control method,when the PIDNN controller based on the improved ant colony algorithm was used to control the heating furnace,the time required for the system to reach the steady state was reduced by about 34%;when the disturbance was added,the time required for the system to return to the steady state was reduced by about 26%,the vibration amplitude was significantly reduced,and the anti-interference ability of the heating furnace control system was enhanced.

关 键 词：蚁群算法 免疫算法 PIT神经网络 加热炉温度控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]