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首页> 外文期刊>Energy and Buildings >Use of genetic algorithms and evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil - Comparison of the AFLC with a standard PID controller
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Use of genetic algorithms and evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil - Comparison of the AFLC with a standard PID controller

机译：利用遗传算法和进化策略开发冷却盘管的自适应模糊逻辑控制器-AFLC与标准PID控制器的比较

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摘要

Experiments conducted on two identical real heating, ventilation and air-conditioning (HVAC) systems were used to compare the performance of an adaptive fuzzy logic controller (AFLC) to that of a conventional proportional, integral and derivative (PID) controller. Two types of experiments were conducted, one with changing set point for supply air temperature and the second with changing supply air flow rate. To remove bias between the testing systems, the controllers were switched from one system to the other.The experimental results indicated that genetic algorithms and evolutionary strategies can be successfully used to develop an adaptive fuzzy logic controller for HVAC applications, potentially saving energy and reducing deviations in the supply air temperature from its set point, but with higher actuator travel distance.
机译：在两个相同的实际供暖，通风和空调（HVAC）系统上进行的实验用于比较自适应模糊逻辑控制器（AFLC）与常规比例，积分和微分（PID）控制器的性能。进行了两种类型的实验，一种是改变送风温度的设定点，另一种是改变送风量的流量。为了消除测试系统之间的偏差，将控制器从一个系统切换到另一个系统。实验结果表明，遗传算法和进化策略可以成功地用于开发适用于HVAC应用的自适应模糊逻辑控制器，从而可以节省能源并减少偏差从设定温度开始调节送风温度，但执行机构的行程更大。

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