蚁群优化控制在变风量空调系统中的应用  

Ant Colony Optimization Control in VAV System

作　　者：朱栋华[1] 李肇蕙[1] 田晓曦[1] 

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

出　　处：《沈阳建筑大学学报（自然科学版）》2012年第6期1131-1135,共5页Journal of Shenyang Jianzhu University：Natural Science

基　　金：国家自然科学基金项目(69874026)

摘　　要：目的寻找一种有效地解决系统惯性大、时间滞后及单独采用PID控制器产生效果不佳的方法,实现温度参量的优化控制.方法根据变风量空调系统的结构,对变风量系统室内温度控制环节采用蚁群优化算法,经过Matlab软件进行仿真,使室内温度控制环节的上升时间、超调量及调整时间得以优化.结果控制环节优化前PID阶跃响应的动态指标为:上升时间tr=50 s,调整时间ts=276 s,超调量σ=30%;经过蚁群优化的PID阶跃响应的动态指标为:上升时间tr=112.5 s,调整时间ts=88 s,超调量σ=3.5%,上升时间相对增加后调整时间和超调量大幅度减小,室内温度控制环节趋于稳定.结论蚁群算法改善了控制环节的超调量、调整时间等问题,提高了系统的自适应性,保证了系统的稳定性和准确性.Looking for a solution which could effectively solve the system＇s inertia,time lag and poor results that were produced by VAV system using PID controller alone,achieving temperature parameter optimization control.According to the VAV air conditioning system＇s structure,the ant colony algorithm is applied to the indoor temperature control link of the system along with using Matlab simulation software simulating in order to rise time,overshoot and adustment time can be optimized about the indoor temperature control link.And then,the dynamic indictors before PID step response of the control link optimization is,the rise time tr=50 s,adustment time ts=276 s,overshoot σ=30%;dynamic indictors of PID step response for the ant colony algorithm,rise time tr=112.5 s,adustment time ts=88 s,overshoot σ=3.5%.After the rise time increased relatively,adjustment time and overshoot reduced drastically,and the link of indoor temperature control has been stabilized.Therefore,Ant colony optimization algorithm can be further applied to the control aspect of the whole system,improving the self-adaptability of the system,and ensuring the stability and accurancy of the system.

关 键 词：蚁群算法 变风量空调系统 PID 优化控制 

分 类 号：O232[理学—运筹学与控制论]