基于改进Homothetic Tube MPC算法的变风量系统温度控制  

Temperature Control of VAV System Based on Improved Homothetic Tube MPC Algorithm

作　　者：杨世忠 刘艳丽 曹会东 YANG Shizhong;LIU Yanli;CAO Huidong(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)

机构地区：[1]青岛理工大学信息与控制工程学院,山东青岛266520

出　　处：《信息与控制》2024年第5期642-651,共10页Information and Control

基　　金：国家自然科学基金(61703224)

摘　　要：VAV(variable air volume)空调系统在温度控制过程中会受到不确定性和随机干扰的影响,同时VAV空调系统具有非线性、复杂性、时变性等特性,传统的控制算法很难保证系统在低能耗的前提下对温度实现精确控制。为了改善传统算法中存在的缺陷,提出将基于受限DNN(deep neural network)的Homothetic Tube MPC(model predictive control)算法应用于VAV空调系统的房间温度控制中:通过对VAV系统进行分析,建立最小能耗目标函数;将实际系统的最优控制问题转化为在线学习问题,利用受限DNN优化算法有效且快速地求解最优控制动作;算法在优化过程中采用大小可变的Tube集来对抗系统运行中存在的不确定性和随机干扰。仿真实验结果表明,对比Tube MPC(TMPC)算法和Homothetic Tube MPC(HTMPC)算法,该控制算法对室内温度的控制精度更高,优化所需的时间成本更低,对不确定因素的适应性更强。实施算法时产生的总能耗比应用TMPC时少15.67%,比采用HTMPC时节约6.46%。The variable air volume(VAV)air conditioning system is influenced by uncertainties and random disturbances during the temperature control process.Furthermore,the VAV system exhibits characteristics such as nonlinearity,complexity,and time variability,which make precise temperature control under low energy consumption challenging for conventional control algorithms.To address these shortcomings in conventional approaches,we propose the application of a homothetic tube model predictive control(MPC)algorithm based on a constrained deep neural network(DNN)for room temperature control in VAV air conditioning systems.By analyzing the VAV system,a minimum energy consumption objective function is established.The optimal control problem of the actual system is transformed into an online learning problem,which is efficiently and rapidly solved using the constrained DNN optimization algorithm.The algorithm employs tubes of variable sizes in the optimization process to counteract uncertainties and random disturbances encountered during system operation.Simulation results indicate that the proposed control algorithm achieves higher precision in indoor temperature control,requires a lower time cost for optimization,and offers stronger adaptability to uncertain factors than the tube MPC(TMPC)and homothetic tube MPC(HTMPC)algorithms.The total energy consumed during the implementation of the proposed algorithm is 15.67%less than that of the TMPC algorithm,and 6.46%less than that of the HTMPC algorithm.

关 键 词：变风量空调系统 鲁棒模型预测控制 深度神经网络 温度控制 能耗 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]