基于改进模糊逻辑的VAV系统GPC控制策略研究  

Research on GPC control strategy of VAV system based on improved fuzzy logic

作　　者：贺宁 李尚 刘利强 高峰[1] HE Ning;LI Shang;LIU Liqiang;GAO Feng(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,西安710055

出　　处：《兵器装备工程学报》2023年第7期297-306,共10页Journal of Ordnance Equipment Engineering

基　　金：国家自然科学基金项目(61903291);陕西省重点研发计划项目(2022N Y-094);博士后面上基金项目(019M660257)。

摘　　要：为了提高多输入多输出变风量(VAV)空调广义预测控制(GPC)系统控制性能,提出一种新型GPC控制策略。该方法通过考虑系统的输出绝对误差和基于误差变化率获得输出初次达到设定值的时间等指标,建立了一个新型二型模糊逻辑模型来在线整定GPC控制器中的加权系数。同时,采用天牛群算法(BSO)来明确二型模糊逻辑输出区间范围,使其可以更贴切地反应系统当前时刻的输出状态。此外,在前述BSO的基础上,提出通过线性以及事件触发地衰减种群规模的方法,缩短BSO的收敛周期,以进一步提升GPC控制策略的性能。实验结果表明:基于二型模糊逻辑整定的GPC控制器性能显著优于已有GPC控制器。In order to improve control performance of the generalized predictive control(GPC) system of multiple-input and multiple-output variable air volume(VAV) air conditioners,this paper proposes a new GPC control strategy.In this method,a new type-2 fuzzy logic(T2FL) model is established to tune the weighting coefficients in the GPC controller online by considering the absolute error of the output and the time for the output to reach the set value obtained for the first time based on the error change rate.At the same time,the beetle swarm optimization(BSO) is used to clarify the output interval range of the T2FL so that it can more closely reflect the output state of the system at the current moment.Besides,based on the BSO method mentioned above,a linear and event-triggered population size decay method is further proposed,which shortens the convergence period of the BSO and further improves the performance of GPC control strategy.The experimental results show that the performance of the GPC controller based on the T2FL tuning is significantly better than that of the existing GPC controllers.

关 键 词：广义预测控制 二型模糊逻辑 BSO算法 空调系统 静压控制 温度调节 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置]