差分粒子群算法在变风量空调末端中的应用  

Application of Differential Particle Swarm Optimization in VAV Air Conditioning Terminal

作　　者：梁芯萌 张九根 谢金鑫[1,2] LIANG Xinmeng;ZHANG Jiugen;XIE Jinxin(College of Electrical Engineering and Control Science,Nanjing Tech University,Nanjing 211816,China;Institute of Intelligent Building,Nanjing Tech University,Nanjing 211816,China)

机构地区：[1]南京工业大学电气工程与控制科学学院,南京211816 [2]南京工业大学建筑智能化研究所,南京211816

出　　处：《电子器件》2019年第5期1269-1273,共5页Chinese Journal of Electron Devices

摘　　要：针对变风量空调末端系统非线性,难以建立精确模型的特点,提出基于改进的差分粒子群算法优化的PID参数整定方法。根据室内温度的实际值与测量值差值的变化,采用惯性权重呈指数形式下降的粒子群算法,使得各个阶段的粒子寻优达到最优;将其与差分算法相结合,利用判断因子对微粒的更新方式进行调控。应用于PID控制器参数优化,并应用于变风量空调系统末端控制。结果表明,该方法稳定时间缩短了10.1 s,超调量减少3.6%。Aiming at the nonlinear,difficult to establish accurate mathematical model of VAV air conditioning terminal control system,a PID parameter tuning method based on improved differential particle swarm optimization algorithm is proposed.According to the actual value of the indoor temperature change difference and the measured value,the inertia weight decreased exponentially with the particle swarm algorithm,the various stages of the particle optimization optimal;The difference algorithm combined with judgement update mode the factor of particulate regulation,give full play to the algorithm of global and local searching ability.After optimization,the method is applied to parameter optimization of PID controller and applied to the control system of VAV air conditioning system.The results show that compared with the traditional PID control,the quality of the control system optimized by this method is improved greatly,the stabilization time of the method is shortened by 10.1 s and the overshoot is reduced by 3.6%.

关 键 词：变风量空调末端 PID控制器 粒子群算法 差分算法 判断因子 闭环控制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.9[自动化与计算机技术—控制科学与工程]