变风量空调末端双闭环系统的模型辨识和仿真

研究变风量空调末端部分控制系统的节能优化问题时,对于末端系统的优化控制应以系统中被控的风阀和房间模型为基础.采用西安建筑科技大学变风量空调实验平台,对末端风阀被控对象采用闭环间接法送行辨识.利用LabView软件对外环温度控制器进行在线仿真设计,创造闭环辨识性条件,建立被控室温房间对象模型.最后,在Sumlink工具箱中用辨识模型进行末端双闭环控制系统的仿真.仿真结果表明,辨识出的模型精确度较高.用于末端节能优化控制研究中可提升控制性能,并为变风量空调节能优化控制提供了参考依据.     

变风量空调系统的迭代学习控制研究

针对变风量（VAV）空调系统下位机设定点变动时,整个系统完全达到稳态时间过长,且各子系统易出现超调的问题,提出采用一种迭代学习控制（ILC）的设定值序列优化方法。以空调系统中变频风机—管道静压控制回路为实例,说明该方法的可行性。采用递推最小二乘法（RLS）建立该回路的动态模型,并给出了一种新的迭代学习期望轨迹,应用迭代学习PD控制律对其动态过程进行仿真分析,并将此算法用于空调实验平台验证其控制效果。结果表明,ILC可以改善空调子系统的动态特性,为VAV空调系统的全局稳态优化奠定了基础。     

变风量空调系统恒温解耦控制优化

在室内恒温控制问题的研究中,变风量空调系统的房间送风量、冷冻水流量和风机转速三个输入变量与房间温度、送风温度和静压三个输出变量之间存在着不同程度的耦合关系,每个房间的温度控制会受到不同程度的干扰,严重时会影响到整个系统的稳定性.为解决上述问题,根据变风量空调系统的房间、表冷器、风机等各个子系统模型,通过寻找一个合适的开环传递函数矩阵,实现对系统的解耦控制,通过使解耦矩阵的对角元素为1,得到简化的解耦矩阵.比通常利用对角化方法和状态反馈矩阵方法直接求得的解耦矩阵要简单.运行空调实验结果表明控制回路之间干扰不明显,解耦控制效果良好.     

工业大系统双层结构预测控制的集中优化与分散控制策略

为降低工业大系统模型预测控制(Model predictive control,MPC)在线计算复杂度,同时保证系统的全局优化性能,提出一种集中优化、分散控制的双层结构预测控制策略.在稳态目标计算层(Steady-state target calculation, SSTC),基于全局过程模型对系统进行集中优化,将优化结果作为设定值传递给动态控制层;在动态控制层,将大系统划分为若干个子系统,每个子系统分别由基于各自子过程模型的模型预测控制进行控制,为减少各子系统之间的相互干扰,在各个子系统之间添加前馈控制器对扰动进行补偿,提高系统的总体动态控制性能.该策略的优点在于能确保系统全局最优性的同时降低了在线计算量,提高了工业大系统双层结构预测控制方法的实时性.仿真实例验证该方法的有效性.     

变风量空调系统房间舒适度温度优化设置

房间温度是变风量空调系统的重要控制量,对舒适度和空调能耗影响很大.房间温度的设置应该考虑室内人员不同的舒适度需求、空气品质和变风量空调系统的节能运行,是一种多目标优化的结果.本文分析了PMV舒适度性能指标,提出便于优化运算的实用舒适度公式.综合考虑舒适度、空气品质和能耗三方面的因素,提出变风量空调系统房间舒适温度优化设置的方法,详细说明优化原理和优化步骤.通过仿真验证了优化方法的有效性,为变风量空调系统房间温度提供了一种可行的设置方法.     

解析解耦设计在变风量空调系统中的应用

研究空调优化控制问题,变风量空调(VAV)具有多变量、强耦合和非线性系统.变量之间的耦合会直接影响空调系统的性能指标和稳定性.针对变风量空调系统进行建模,模型为三输入三输出的传递函数矩阵.在内模控制系统结构基础上,运用解耦控制的解析设计方法来设计解耦控制器.解耦设计方法可以达到标称系统变量间完全解耦,并且参数可以在线调整,使输出能够紧密跟随系统的动态变化.仿真结果表明,解耦控制器的设计提高了性能,应用效果较好.     

压力有关型VAVBOX控制回路的系统辨识

变风量空调末端（VAVBOX）是变风量系统的关键部位,其运行的状况将直接影响空调系统的整个运行效果。本文主要论述了对压力有关型变风量末端控制回路进行了系统辨识,风阀模型采用开环辨识的方法,房间模型辨识中采用了闭环辨识。为研究变风量空调末端（VAVBOX）在室内温度方面的精确控制与抗扰问题打下了坚实基础。     

自适应解耦补偿控制在变风量空调系统中的应用

针对变风量（VAV）空调系统正常运行的必要条件——稳定性问题进行研究,把变风量空调系统基于分解协调的策略合理地分解为机组部分和末端部分。应用了单神经元自适应PID控制器的概念,并结合神经元自适应解耦控制的策略,对变风量空调系统的机组部分进行了解耦与控制,还从实验系统测试的方面对解耦控制策略进行了验证。实验结果证明了这种解耦控制策略的有效性。     

Lonworks技术在变风量中央空调解耦控制系统中的应用

该文采用现场总线技术中的Lonworks技术建立变风量空调解耦控制系统,并对该变风量空调解耦控制系统作了较为细致的描述,提出了用LonManagerDDEServer和VisualBasic建立变风量空调解耦控制系统的LONWORKS网络监控系统的方法。认为用Lonworks技术中的网络变量来处理各个控制回路之间的解耦补偿系数是很合适的。该系统具有现场总线技术的优点,包括实现了系统控制的彻底分散化,提高了系统的可靠性。用Lonworks技术对变风量空调系统进行控制,可以极大地改善变风量空调控制系统的品质并提高控制系统的先进性。     

基于LabVIEW的空调水系统优化控制策略研究

在对空调水系统中的压差控制与温差控制两种控制策略进行分析后,为提高系统节能效果,将串级控制方法引入到空调水系统控制中;以西安建筑科技大学变风量空调实验室内的冷冻水系统为对象,提出用最小二乘法辨识串级回路主、副环的传递函数模型,并在此基础上,基于LabVIEW软件对控制系统进行仿真分析和实验研究;仿真和实验结果证明了串级控制策略在空调水系统控制中的有效性和良好的应用价值。     

Distributed model predictive control over network information exchange for large-scale systems

A class of large scale systems, which is naturally divided into many smaller interacting subsystems, are usually controlled by a distributed or decentralized control framework. In this paper, a novel distributed model predictive control (MPC) is proposed for improving the performance of entire system. In which each subsystem is controlled by a local MPC and these controllers exchange a reduced set of information with each other by network. The optimization index of each local MPC considers not only the performance of the corresponding subsystem but also that of its neighbours. The proposed architecture guarantees satisfactory performance under strong interactions among subsystems. A stability analysis is presented for the unconstrained distributed MPC and the provided stability results can be employed for tuning the controller. Experiment of the application to accelerated cooling process in a test rig is provided for validating the efficiency of the proposed method.     

Nash-based robust distributed model predictive control for large-scale systems

In this paper, a new robust distributed model predictive control (RDMPC) is proposed for large-scale systems with polytopic uncertainties. The time-varying system is first decomposed into several interconnected subsystems. Interactions between subsystems are obtained by a distributed Kalman filter, in which unknown parameters of the system are estimated using local measurements and measurements of neighboring subsystems that are available via a network. Quadratic boundedness is used to guarantee the stability of the closed-loop system. In the MPC algorithm, an output feedback-interaction feedforward control input is computed by an LMI-based optimization problem that minimizes an upper bound on the worst case value of an infinite-horizon objective function. Then, an iterative Nash-based algorithm is presented to achieve the overall optimal solution of the whole system in partially distributed fashion. Finally, the proposed distributed MPC approach is applied to a load frequency control (LFC) problem of a multi-area power network to study the efficiency and applicability of the algorithm in comparison with the centralized, distributed and decentralized MPC schemes.     

Distributed model predictive control for polytopic uncertain systems subject to actuator saturation

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min–max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm.     

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

This paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.     

变风量空调系统末端的变论域模糊PID控制

针对目前传统PID控制对模型依赖性强,参数难以在线调整,对不确定性强的变风量(VAV)空调系统的控制快速性和准确性差的特点,提出一种变论域模糊PID控制,以提高系统的控制速度和精度,使系统具有良好的动、静态性能。在推导的变风量空调房间和末端装置数学模型的基础上进行了仿真研究。结果表明,控制系统具有很好的鲁棒性和自适应能力,可以明显提高系统的响应速率,控制器的动态结构更适用于变风量空调系统。     

变风量空调系统的分布式预测控制

基于预测控制策略的多输入多输出（MIMO）控制器可以解决单通道变风量系统的控制问题,但在输入输出变量较多时,计算就变得非常复杂。在分析系统工作机理和系统动力学行为的基础上,建立了内部模型,在此基础上采用分布式预测控制技术将系统分解成四个子系统,通过设定参考轨迹、输入输出约束及加权矩阵,解决了系统凸二次型的全局优化和纳什问题。通过仿真研究验证了系统控制的效果。     

Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes

In this paper, two novel networked model predictive control schemes based on neighbourhood optimization are presented for on-line optimization and control of a class of serially connected processes (known as the cascade processes in some references), in which the on-line optimization of the whole system is decomposed into that of several small-scale subsystems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each subsystem to exchange information with its neighbour subsystems. An iterative algorithm for networked MPC and a networked MPC algorithm with one-step delay communication are developed according to different network capacities. The optimality of the iteration based networked MPC algorithm is analyzed and the nominal stability is derived for unconstrained distributed control systems. The nominal stability with one-step delay communication is employed for distributed control systems without the inequality constraints. Finally, an illustrative example and the simulation study of the fuel feed flow control for the walking beam reheating furnace are provided to test the effectiveness and practicality of the proposed networked MPC algorithms.     

Augmented Lagrangian Pattern Search Based Multi‐Agent Model Predictive Control of Rhine‐Meuse Delta

In Large Scale Systems the concept of centrality fails due to the lack of centralized computing capability. The control of such systems has to be performed using multiple control agents. In this case, the matter of interactions among neighboring subsystems needs to be considered. In this paper, a water control system in the Netherlands is studied as a real large scale system. A multi‐agent scheme is applied to control the flow through the system which is decomposed into two interconnected subsystems. Each agent employs a model‐based predictive control (MPC) technique. The model of this large scale system is nonlinear and nonconvex. Therefore, an augmented Lagrangian pattern search optimization algorithm is used to implement multi‐agent MPC for this system. This proposed algorithm is applied by each control agent to solve its own interconnected optimization problem, at each subsystem of whole the water system. Simulation results show the effectiveness of the proposed approach.