基于模糊自适应控制的温室温度调控

为了实现温室在冬季时温度控制精度高、能耗小的目标。该研究提出了一种将热量平衡原理与模糊控制相结合的模糊自适应控制方法，在模糊控制器中加入基于热量平衡方程的输出隶属度函数修正模块，通过监测温室内外温度数据，在上位机中对模糊控制器中的输出隶属度函数进行自适应调整，最终使温室温度稳定在目标温度。结果表明，基于热量平衡方程式的温室温度模糊自适应控制系统具有较好的稳定性和准确性，在设置20℃为目标温度值的情况下，基于热量平衡方程的模糊自适应控制最终使温室温度稳定在(19.8±0.11)℃，一般模糊控制方法最终使温室温度稳定在(13.5±0.5)℃，无法达到目标环境温度值，而阈值控制最终使温室温度稳定在(20.0±0.85)℃；同时，基于热量平衡方程式的温室温度模糊自适应控制能耗较低，且能量利用率更高，模糊自适应控制能量利用率为45.97%，而阈值能量利用率仅为20.21%。研究提出的基于热量平衡方程的模糊自适应控制方法不仅满足温室温度调控需求，而且能够提高能量利用率，降低能耗。

Greenhouse temperature control using fuzzy adaptive control

A fuzzy adaptive control system was presented for greenhouse temperature, according to heat balance and fuzzy control. The accuracy of temperature control was improved to reduce the energy consumption of the control system in the winter greenhouse. An adaptive adjustment module was introduced for the output membership functions using the heat balance equation in the system. The membership functions of output variables were real-time adjusted with the outdoor temperature in the upper computer. The target temperature was ultimately reached to be stable in the greenhouse. At the same time, the experiment was conducted to verify the fuzzy adaptive control. The final experimental results were as follows: (1) The temperature was set to be 30, 35, 40, and 45 ℃ for the water tank of the heating fan. The temperature inside the greenhouse was then monitored for a period of time. The monitoring data was substituted into the energy balance equation to calculate the comprehensive heat transfer coefficient of the heating fan. 19 datasets showed that the comprehensive heat transfer coefficient of the heating fan was 50.50 W/(m²·℃). (2) A fuzzy adaptive control system was developed for greenhouse temperature using Python. A comparison was made on the control effects of fuzzy control, adaptive fuzzy, and threshold control. The more sensitive output response of the fuzzy adaptive control was observed at the target temperature of 20 ℃, according to the heat balance equation. The higher temperature was found in the water tank of heating fan at the beginning of the control. The overall control time was shorter at 35 min. Finally, the temperature of the temperature chamber was stabilized at (19.8 ± 0.11) ℃. There was a longer control of 39 min for the general fuzzy controller. Finally, the greenhouse temperature was stabilized at (13.5 ± 0.5) ℃, which was unable to reach the target ambient temperature. The threshold control had the shortest control time of 26 min. But there were more fluctuations to reach the target temperature until the greenhouse temperature was stabilized at (20.0 ± 0.85) ℃. (3) The ratio of heat input was calculated from the heating fan to the total energy consumption of the greenhouse using different control systems. The energy utilization rate of fuzzy adaptive control was 45.97% when the total energy consumption was 1.584 × 10⁷ J. While the energy utilization rate of threshold control was only 20.21% when the total energy consumption was 3.301 × 10⁷ J. Therefore, low energy consumption, high stability, and accuracy were achieved in the fuzzy adaptive control system with the heat balance equation, fully meeting the needs of temperature control in a winter greenhouse.