自适应模糊PID控制器的研究

除了开／关控制,常规PID控制器是HVAC系统现场控制中应用最为广泛最基本的控制器,但对于非线性、时变的系统,常规的PID控制则很难达到满意的控制效果。本文提出了自适应模糊PID控制器模型,可以根据系统误差和误差变化率不断对PID控制器的输出进行在线调整。以某空调房间温度控制系统为研究对象,对自适应模糊PID控制器和常规PID控制器进行了对比。计算结果表明,自适应模糊PID控制器具有良好的动、静态性能,并且鲁棒性较强。     

HVAC system optimization with CO2 concentration control using genetic algorithms

This study describes the use of genetic algorithms (GAs) for operating standard HVAC systems (HVAC—heating, ventilation and air conditioning) in order to optimize performance, primarily with regard to power saving. Genetic algorithms were introduced as an instrument for solving optimization problems. Analytic optimization procedures are widely used in other fields of engineering, but they are difficult to operate within HVAC systems, because the range of the research is usually too broad, the problems are not linear but rather discontinuous, and they mostly have complex limitations. This is why for this type of system genetic algorithms are used, since they have the qualities of robustness and efficiency that are crucial for finding the optimal solution. A simulation is conducted in order to demonstrate how much power can be saved by using the suggested method of CO₂ concentration control in a standard HVAC system. In addition to Matlab Simulink, the suggested method is verified with Energy software.     

Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques

In this paper, we present a novel intelligent coordinator control system based on hierarchical structure. The presented structure consists of one coordinator and five fuzzy logic controllers. The intelligent coordinator architecture contains uses a master and a slave agent. The master agent is an economy behavior fuzzy system and the slave agent consists of two fuzzy negotiation machines (FNMs) and one decision logic unit. Type-1 FS models are chosen to characterize the concepts of thermal comfort, illuminance and carbon dioxide concentration. The word “comfort” is represented by a 3D fuzzy set in fuzzy cube. Using the symmetric fuzzy equality measure we can measure the membership grade of the 3D fuzzy comfort set. We present the structure of a fuzzy controller-agent (FCA) and propose the tuning of parameters of the FLC by genetic algorithms (GAs). The simulation results show that the proposed intelligent control system successfully manages the users’ preferences for thermal and illuminance comfort, indoor air quality and the energy conservation.     

Fuzzy control model and simulation of supply air system in a test rig of low-temperature hot-water radiator system

This paper proposes a typical multi-variable, large time delay and nonlinear system, self-extracting rules fuzzy control (SERFC) method to maintain a stable temperature value in a built environment chamber with supply air system and hot-water system. The parameters of the transfer functions in every control loop were identified by experimental data in a format of time sequences obtained from the experiment of dynamical responding performance. Fuzzy control simulations were implemented based on adjustment of the supply air system and hot-water system by SERFC. The simulation results show that SERFC for environment chamber has satisfied performance. There is no higher overshoot and stable error. The work presented in here can be used to deal with those complex thermal processes with difficulties in modeling of fuzzy control rules and provide a foundation for further application of fuzzy control in HVAC system.     

变风量空调系统温度模糊PID控制

将模糊PID控制应用于变风量空调系统中,任务是将送风温度和空调房间内的温度（回风温度）控制在各自的设定目标值附近。分别设计了变风量空调系统的送风温度模糊PID控制系统和室内温度（视为回风温度）模糊PID控制系统,通过调节冷冻水阀门的开度来控制送风温度,通过调节变频风机的转速来控制室内温度。应用所设计的模糊PID控制器对送风温度和空调房间的温度（即回风温度）进行了实时在线控制,控制结果表明模糊PID控制器设计合理,控制效果良好。     

Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel’s daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy.     

Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system

In this study, a heating, ventilating and air-conditioning (HVAC) system with different zones was designed and tested. Its fan motor speed and damper gap rates were controlled by two controllers (i.e. a PID controller and an intelligent controller) in real time to minimize its energy consumption. The desired temperatures were realized by variable flow-rate by considering the ambient temperature for each zone and evaporator. The PID parameters obtained in our previous theoretical work using fuzzy logic were utilized in this study. The experimental data used in this study was collected using a HVAC system built in a laboratory environment. The fan motor speed and damper gap rates were predicted using wavelet packet decomposition (WPD), entropy, and neural network (NN) techniques. WPD was used to reduce the input vector dimensions of the intelligent model. The suitable architecture of the NN model is determined after certain trial and error steps. According to test results, the developed model performance is at desirable level. Efficiency of the developed method was tested and a mean 95.62% recognition success was obtained. This model is an efficient and robust tool to predict damper gap rates and fan motor speed to minimize energy consumption of the HVAC system.     

利用模糊逻辑和遗传法对高层建筑结构进行主动控制

近年来土木工程结构上采用的主动调谐质量阻尼(ATMD)控制系统开始引起研究者们极大的关注。强调利用遗传法和模糊逻辑(GFLC)的组合作用,在ATMD控制系统的设计和优化措施中选择合适的参数,以使得在地震激励作用下建筑的反应最小化。因此,提出的方法,在处理不可预知的非线性现象方面,具有模糊逻辑控制(FLC)和遗传法(GAs)的双重优势。模型采用剪切型框架,并且在状态空间下解决问题。这种方法被应用在位于伊朗共和国的雷什特城的一个11层高的实际建筑上。从这种控制系统中得到的结果,与采用TMD方案和线性二次调节(LQR)控制方法中得出的结果相对比发现,GAs和FLC的综合使用对减少建筑在地震作用下的反应非常有效。     

自动控制在暖通空调系统中的发展与应用

介绍分析了自动控制技术在暖通空调工程中的应用与发展趋势,重点介绍和总结了PID控制、模糊控制、神经网络控制、遗传算法等现代控制算法在空调领域的应用情况,比较了这些自动控制技术的优缺点,提出空调自动化控制对于节能的研究方向。     

华商超市冰蓄冷空调系统设计

介绍了冰球式蓄冷技术在该超市中的应用。该系统采用乙二醇溶液直接进入末端设备的大温差供水技术，取消了制冷用板式换热器，设计了蓄冷专用水泵，并采用了自动控制系统，工程造价低于常规电制冷空调，运行费用节省近40％。     

一次回风空调箱冷却除湿过程计算

分析了舒适性空调系统中由空调箱选型不当引起的空气处理偏离设计状态点的现象。验证了风机盘管表冷器修正公式应用于空调箱的可行性。提出了空调箱特性指标(S0B00.162)的概念,建立了一次回风系统空调箱空气热湿处理过程数学模型,并使用MATLAB软件编程求解模型,在一定设计条件下计算出空调箱的出风参数、供回水温差,并以供回水温差作为空调箱选型的依据。运用模型对两个气候差异较大地区的候车厅空调箱进行选型计算。建立的数学模型及求解方法同时可作为空调箱运行调节的依据。     

Research on the control laws of the electronic expansion valve for an air source heat pump water heater

Compared to the conventional air conditioner, the air source heat pump water heater (ASHPWH) possesses wider operating ranges and more dramatic changes in working conditions. Conversely, traditional throttle devices, such as the thermostatic expansion valve (TEV) and capillary tube, are restricted by narrow regulating ranges in refrigerant mass flow rate and lagging response to the superheat. This article incorporates a novel dual-fuzzy-controller to regulate the electronic expansion valve (EEV) specialized for the ASHPWH system. The study analyzes the effects of the EEV initial opening and the target superheat on the performance of the ASHPWH. Moreover, this research proposes a fuzzy control method of selecting the initial opening and the target superheat on the basis of the ambient temperature and water temperature, and employs superheat error (e) and the derivation of superheat error (ec) as the input variables of the fuzzy controller B to regulate the opening of the EEV during steady running process. To improve self-adaptability of the fuzzy controller, a rule modifier and a gain scheduler are introduced. In order to quantitatively reflect the difference in the performance between the TEV-controlled system and EEV-controlled one, experimental comparison between the EEV and the TEV is presented. Results demonstrate that both the stability and efficiency of the ASHPWH can be improved significantly by the EEV.     

A new predictive thermal sensation index of human response

The purpose of this paper is to investigate the problem of determining a human thermal sensation index that can be used in feedback control of HVAC systems. We present a new approach based on fuzzy logic to estimate the thermal comfort level depending on the state of the following six variables: the air temperature, the mean radiant temperature, the relative humidity, the air velocity, the activity level of occupants and their clothing insulation. The new fuzzy thermal sensation index is calculated implicitly as the consequence of linguistic rules that describe human's comfort level as the result of the interaction of the environmental variables with the occupant's personal parameters. The fuzzy comfort model is deduced on the basis of learning Fanger's `Predicted Mean Vote' (PMV) equation. Unlike Fanger's PMV, the new fuzzy PMV calculation does not require an iterative solution and can be easily adjusted depending on the specific thermal sensation of users. These characteristics make it an attractive index for feedback control of HVAC systems. The simulation results show that the new fuzzy PMV is as accurate as Fanger's PMV.     

变风量系统中空气处理机AHU的控制

本文主要阐述了VAV系统中AHU的控制。笔者结合工程经验给出了AHU送风温度控制的注意事项,并对定静压控制法、变静压控制法、总风量法三种控制方法加以比较,为变风量空调系统的控制设计提出了一些参考性建议。     

Multivariable control of indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system

Controlling indoor humidity at an appropriate level is very important since this affects occupants' thermal comfort and indoor air quality (IAQ). The paper presents an investigation on developing a multi-input multi-output (MIMO) control strategy for simultaneously controlling the indoor air temperature and humidity by varying the speeds of both compressor and supply fan in an experimental DX A/C system. The MIMO-based controller was designed based on the linearized dynamic model of the experimental DX A/C system. The Linear Quadratic Gaussian (LQG) technique was used in designing the MIMO-based controller. The controllability tests with respect to both the disturbance rejection capability and the command following capability were carried out to assess the control performance of MIMO controller. The results of disturbance rejection capability test showed that the MIMO control strategy can effectively maintain the indoor air temperature and humidity to their respective settings after an unmeasured heat load disturbance was imposed by simultaneously varying speeds of both the compressor and the supply fan of the DX A/C system. Furthermore, in the command following capability test for indoor air temperature, the test results showed that the indoor air temperature can be controlled to its new setting while indoor humidity remained unchanged. Similar test results were also observed in the command following capability test for indoor humidity. Therefore, the MIMO controller developed can effectively control indoor air temperature and humidity simultaneously by varying compressor speed and supply fan speed of the DX A/C system. Compared to the previous related studies using conventional on–off control method or single-input single-output (SISO) control strategy, which can only effectively control either air temperature or relative humidity, the MIMO controller can simultaneously control the indoor air temperature and humidity with adequate control sensitivity and accuracy. The application of MIMO control strategy developed can be extended to other HVAC systems in the future to improve their operating performance and energy efficiency.     

Quasi-adaptive fuzzy heating control of solar buildings

Significant progress has been made on maximising passive solar heat gains to building spaces in winter. Control of the space heating in these applications is complicated due to the lagging influence of the useful solar heat gain coupled with the wide range of construction materials and heating system choices. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set-up and commissioning procedures. This paper addresses the development and testing of a quasi-adaptive fuzzy logic control method that addresses these issues. The controller is developed in two steps. A feed-forward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then designed to have two inputs: the first input being the error between the set-point temperature and the internal air temperature and the second the predicted future internal air temperature. The controller was implemented in real-time using a test cell with controlled ventilation and a modulating electric heating system. Results, compared with validated simulations of conventionally controlled heating, confirm that the proposed controller achieves superior tracking and reduced overheating when compared with the conventional method of control.     

Optimal supply air temperature with respect to energy use in a variable air volume system

In a variable air volume (VAV) system with 100% outdoor air, the cooling need in the building is satisfied with a certain air flow at a certain supply air temperature. To minimize the system energy use, an optimal supply air temperature can be set dependent on the load, specific fan power (SFP), chiller coefficient of performance, outdoor temperature and the outdoor relative humidity. The theory for an optimal supply air temperature is presented and the heating, ventilation and air-conditioning (HVAC) energy use is calculated depending on supply air temperature control strategy, average U-value of the building envelope and two outdoor climates. The analyses show that controlling the supply air temperature optimally results in a significantly lower HVAC energy use than with a constant supply air temperature. The optimal average U-value of the building envelope is in practise mostly zero.     

Bilevel Parallel Genetic Algorithms for Optimization of Large Steel Structures

This article is concerned with optimization of very large steel structures subjected to the actual constraints of the American Institute of Steel Construction ASD and LRFD specifications on high-performance multiprocessor machines using biologically inspired genetic algorithms. First, parallel fuzzy genetic algorithms (GAs) are presented for optimization of steel structures using a distributed memory Message Passing Interface (MPI) with two different schemes: the processor farming scheme and the migration scheme. Next, two bilevel parallel GAs are presented for large-scale structural optimization through judicious combination of shared memory data parallel processing using the OpenMP Application Programming Interface (API) and distributed memory message passing parallel processing using MPI. Speedup results are presented for parallel algorithms.     

Thermal comfort and IAQ assessment of under-floor air distribution system integrated with personalized ventilation in hot and humid climate

The potential for improving occupants’ thermal comfort with personalized ventilation (PV) system combined with under-floor air distribution (UFAD) system was explored through human response study. The hypothesis was that cold draught at feet can be reduced when relatively warm air is supplied by UFAD system and uncomfortable sensation as “warm head” can be reduced by the PV system providing cool and fresh outdoor air at the facial level. A study with 30 human subjects was conducted in a Field Environmental Chamber. The chamber was served by two dedicated systems – a primary air handling unit (AHU) for 100% outdoor air that is supplied through the PV air terminal devices and a secondary AHU for 100% recirculated air that is supplied through UFAD outlets. Responses of the subjects to the PV-UFAD system were collected at various room air and PV air temperature combinations. The analyses of the results obtained reveal improved acceptability of perceived air quality and improved thermal sensation with PV-UFAD in comparison with the reference case of UFAD alone or mixing ventilation with ceiling supply diffuser. The local thermal sensation at the feet was also improved when warmer UFAD supply air temperature was adopted in the PV-UFAD system.     

Neural network model incorporating a genetic algorithm in estimating construction costs

This paper applies the back-propagation network (BPN) model incorporating genetic algorithms (GAs) to cost estimation. GAs were adopted in the BPN to determine the BPN's parameters and to improve the accuracy of construction cost estimation. Previously, there have been no appropriate rules to determine these parameters. The construction cost data for 530 residential buildings constructed in Korea between 1997 and 2000 were used for training and evaluating the performance of the model. This study showed that a BPN model incorporating a GA was more effective and accurate in estimating construction costs than the BPN model using trial and error.