基于改进GA的变风量空调系统优化控制仿真

Optimization Control of Vav Air Conditioning System Based on Improved Genetic Algorithm

针对变风量空调系统本身存在多变量、非线性、强耦合等问题,基本遗传算法很难起到很好的控制效果。现针对基本遗传算法的适应度值、交叉率和变异率进行改进,通过引入Sigmoid函数和高斯分布函数自适应调整公式来调整交叉率和变异率,并对适应度值进行自适应设计,构建一种改进遗传算法。并采用改进遗传算法对PID控制器参数进行在线调整,最后将改进的遗传PID控制器和基本遗传PID控制器分别对典型二阶系统、滞后系统进行控制仿真,并对变风量空调系统的冷冻水流量-送风温度,送风温度-房间温度,风机频率-风管静压这三个控制回路进行仿真研究;仿真结果表明:所提算法的动态性能好,系统超调量、上升时间均有所减少,同时具有较强的抗干扰性和鲁棒性,为变风量空调系统优化控制提供了一种可行控制方案。

Due to the problems of variable air volume(VAV)air-conditioning systems,such as multivariable,nonlinear and strong coupling,the basic genetic algorithm is difficult to achieve a good control effect.In this paper,the basic genetic algorithm is improved.By introducing the sigmoid function and gaussian distribution function and combining with the fitness function of the genetic algorithm,the fitness value,crossover rate and mutation rate of the basic genetic algorithm were improved to construct the improved genetic algorithm.The improved genetic algorithm for PID controller parameters was adjusted online.Finally,the improved genetic PID controller and the basic PID controller were used to control and simulate the typical second-order system and lagging system respectively.The three control loops of the VAV air conditioning system were simulated:frozen water flow-supply air temperature,supply air temperature-room temperature,fan frequency-duct static pressure.The simulation results show that the proposed algorithm has good dynamic performance,less overshoot and rise time,and strong anti-interference and robustness,providing a feasible method for optimal control of the VAV air-conditioning system.