基于改进BSO算法的VAV空调系统GPC参数整定  

GPC Parameter Tuning of VAV Air Conditioning System Based on Improved BSO Algorithm

作　　者：贺宁 李俊辰 李尚 郝文斌 HE Ning;LI Junchen;LI Shang;HAO Wenbin(School of Mechanical and Electrical Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学机电工程学院,陕西西安710055

出　　处：《控制工程》2024年第6期1114-1122,共9页Control Engineering of China

基　　金：国家自然科学基金资助项目(61903291)。

摘　　要：针对变风量(variable air volume,VAV)空调系统具有大滞后、强耦合等控制难点,提出了一种基于改进天牛群优化(beetle swarm optimization,BSO)算法的VAV空调系统广义预测控制(generalized predictive control,GPC)参数整定方法。针对标准GPC整定算法只调节控制加权系数λ导致控制性能不佳的问题,首先,提出一种改进BSO算法,通过动态化选取权重参数与学习因子获得较佳的寻优性能。其次,提出一种基于改进BSO算法的新型GPC参数整定方法,可同时整定控制器的控制加权系数λ、控制时域Nu、预测时域Np及柔化系数α。最后,结合实际VAV空调系统,开展仿真与半实物实验。结果表明,采用改进BSO算法调优后GPC的控制效果得到显著提升,调节时间仅为7 s,超调量仅为0.28%。For the control difficulties of variable air volume(VAV)air conditioning system,such as large delay and strong coupling,a novel algorithm based on modified beetle swarm optimization is proposed.generalized predictive control(GPC)parameter setting method for VAV air conditioning system based on beetle swarm optimization(BSO)algorithm.For the problem that the standard GPC tuning algorithm only adjusts the control weight coefficient λ,which leads to poor control performance,an improved BSO algorithm is proposed to obtain better optimization performance by dynamically selecting weight parameters and learning factors.Secondly,a new GPC parameter tuning method based on improved BSO algorithm is proposed,which can simultaneously adjust the control weight coefficientλ,control time domainuN,prediction time domainpNand flexibility coefficientαof the controller.Finally,combined with the actual VAV air conditioning system,simulation and semi-physical experiments are carried out.The results show that the control effect of GPC with improved BSO algorithm is significantly improved,the adjustment time is only 7 seconds,and the overshoot is only 0.28%.

关 键 词：广义预测控制 变风量空调系统 BSO算法 控制器参数整定 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]