【作者】 申欢迎；

【导师】 秦萍；

【作者基本信息】 西南交通大学 ， 供热、供燃气、通风及空调工程， 2004， 硕士

【摘要】 随着国民经济的发展以及人们生活水平的提高，人们对室内热舒适度的要求也越来越高，而单纯以室内温度为被控参数的传统控制方法（室内设定温度值恒定）显得过于粗糙；且暖通空调系统具有很强的非线性特征，传统的PI、PID等线性控制理论难以获得精确的数学模型。针对这些问题，国内外学者提出根据PMV指标偏差及偏差变化率，应用模糊控制方法实时修正室内设定温度值的方法和基于新有效温度偏差及偏差变化率的模糊控制方法；这些控制方法实际上都属反馈控制方法，系统的控制作用会有滞后，为了改善系统的控制性能，可采用基于扰动的前馈控制（又叫扰动补偿），但是，一个控制系统不能单纯用前馈控制，因为这要求对系统的所有扰动都要进行控制，这往往是难以做到的。本文将反馈（PMV指标偏差）和前馈结合起来，只对空调系统的主要扰动（室内冷负荷）进行补偿，提出基于PMV指标偏差及室内冷负荷的舒适空调模糊控制方浊，对各种气象条件的仿真结果表明，此方法对空调系统具有良好的控制效果，而且更加节能。 本文通过分析环境因素对PMV指标的影响，得出以下结论：ι_a，ι_r、φ_a升高均将导致PMV指标增大：在ι_a＜人体表面平均温度ι_cl时，V_a升高将导致PMV指标减小，在ι_a=ι_cl时，V_a对PMV无影响，在ι_a＞ι_cl时，V_a升高将导致PMV指标增大，且PMV随V_a变化的变化率不同；V_a＜0.1m／s时风速对PMV几乎无影响，V_a＜0.2m／s时风速对PMV的影响也不大，基本上可忽略，本文提出在现有风速传感器对低风速测量精度不高的实际情况下，对低风速（我国暖通空调设计规范规定：夏季舒适空调风速不应大于0.3m／s）且风速变化不大的舒适空调环境，在计算PMV指标时，完全可将V_a设定为常数0.2m／s。 本文首先建立了气象模型、客流量模型、冷负荷模型、房间模型、表冷器模型及PMV指标预测器，并编制了相应的子程序：之后建立了基于PMV指标偏差及室内冷负荷的模糊控制模型，并编制了基于该控制模型的空调系统仿真主程序，同时也编制了传统的固定温度的PID控制模型和传统的固定温湿度的定风量再热控制模型的空调系统仿真主程序，在MATLAB环境中模拟仿真了三种不同控制方法在各种气象条件下室内环境参数以及系统冷负荷、送风量的动态变化曲线。结果表明，基于PMV指标偏差及室内冷负荷的舒适空调模糊控制方法控制作用及时，可以将PMV指标控制在舒适范围内和最大程度地接近舒适范围的上限，与另两种方法相比，实现了空调系统的节能运行，且具有比于航等人提出的基于新有效温度偏差及偏差变化率的模糊控制方法更高的节能效果。因此本文提出的控制方法具有一定的可行性和实用价值。更多还原

【Abstract】 With the development of the national economy and increase of people’ s life level, people make an request for the high indoor thermal comfort, however, the control project in which the indoor temperature is unique controlled parameter is not enough. The HVAC system has strong non-1 inear characteristic, and it is difficult to gain the preri.se mathematical model by using the traditional linear control theory, such as PI,PID. Aim at these problems, many scholars have put forward to the method that revises indoor set-temperature value with according to the difference of PMV index and the cHange-rate of its difference, applying fuzzy control. These control methods belong to the feedback control method, the control function of system may be delayed. For the sake of improving control function, the feed front control method based on interfered factors can be adopted. But a control system can’ t apply the unique feedfront control, because this request that all interfered factors of system, and this is usually difficult. Combining the feedback control with the feedfront control, the paper put forward to the comfortable air-conditioning fuzzy control system in which the difference of PMV index and the indoor cooling load are input parameters. The paper has simulated the change of indoor thermal environmental parameter and the energy consumption of system under the variety weather circumstances. The results show that the air-conditioning system can be effectively control and the energy consumption of system is economical.The paper analyzed that the environmental factor influences PMV index, and draw these conclusions:ta,tr, a go up that will cause PMV index enlarged; When t, lower human body surface mean temperature, V go up that will cause PMV index reduced; When V, lower 0.1m/s, the wind velocity has no the influence on PMV index. The paper put forward that the influence of the wind velocity to PMV index can be neglected and set up it as a constant (such as 0. 2 m/ s) in the air-conditioning environment with the low wind velocity.The paper, first, established the weather model, the persons flow rate model, the load model, the room model, the cold-water heat exchanger model etc. Second, established the control model based on the difference of PMV index and the indoor cooling load, and established the simulating main program of air-conditioning system. The results of simulation show this control method is on time, and can control PMV index that is satisfied and realize the more economical energy carry of the air-conditioning system.This control method has great feasibility and practical value.更多还原