论文标题
NFT水培控制使用Mamdani模糊推理系统
NFT Hydroponic Control Using Mamdani Fuzzy Inference System
论文作者
论文摘要
营养膜技术（NFT）方法是最流行的水培培养方法之一。该方法具有更轻松的维护，更快，最佳的植物生长，更好地使用肥料以及减少沉积。 NFT的缺点包括消耗电力和疾病的更快传播。因此，NFT需要良好的营养控制和监测系统，以节省电力并实现对害虫和疾病的最佳生长和抵抗力。在这项研究中，设计了一种养分控制，并具有pH和TDS水平的指标，并配备了物联网（IoT）的监测系统。使用的控制系统是Mamdani模糊推理系统。系统的输出是pH，pH下降和AB混合营养泵的活跃时间，旨在使营养液体的pH和TD归一化。实验结果表明，需要一到三个控制步骤才能使pH归一化。一个控制步骤的响应时间为60秒，并且可以防止pH上升和pH下降。至于TDS控制，AB混合泵活动时间的预测可以准确地工作，并且可以在一个控制步骤中对TDS水平进行标准化。总体而言，基于表面控制，模拟和实际实验数据，指示控制系统运行良好，并且可以将pH和TD归一化为所需的正常标准。
The Nutrient Film Technique (NFT) method is one of the most popular hydroponic cultivation methods. This method has advantages such as easier maintenance, faster and optimal plant growth, better use of fertilizers, and less deposition. The disadvantages of NFT include the consumption of electrical power and the faster spread of disease. Therefore, NFT requires a good nutrient control and monitoring system to save electricity and achieve optimal growth and resistance to pests and diseases. In this study, a nutrient control was designed with indicators of pH and TDS levels and equipped with an Internet of Things (IoT) based monitoring system. The control system used is the Mamdani Fuzzy Inference System. The output of the system is the active time of the pH Up, pH Down, and AB Mix nutrient pumps, which aim to normalize the pH and TDS of nutrient liquids. The experimental results show that one to three control steps are needed to normalize pH. One control step has a response time of 60 seconds, and it can prevent pH Up and pH Down oscillations. As for TDS control, the prediction of AB mix pump active time works accurately, and TDS levels can be normalized in one control step. Overall, based on surface control, simulations, and real experimental data, it is indicated that the control system operates very well and can normalize pH and TDS to the desired normal standard.
