Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97806
標題: 模糊粒子群演算法在微環境控制之應用
Micro-environment Control by Using Fuzzy-PSO Method
作者: 林柏諺
Po-Yen Lin
關鍵字: 微環境控制
模糊理論
模糊粒子群控制
Microenvironment control
Fuzzy rule
Fuzzy-PSO algorithm
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摘要: 微環境控制近年來發展蓬勃,受到學術界及業界的重視,世界各地紛紛投入資源,爭相開發微環境系統,用以改變農業生產模式,提高農作生產效率與能力,並同步將農村人力不足與地球環境變遷的影響降至最低。本論文運用微環境控制箱搭配模糊粒子群演算法(Particle swarm optimization, PSO)建置微環境的控制模式與能力,兼顧能源應用效率,創造出最有利於作物生長與管理的人造環境。 本研究使用非色散式紅外線溫濕二氧化碳感測器監測微環境控制箱環境數據,並將資料回傳至由MATLAB軟體所建構之即時監看人機介面,再透過模糊控制(Fuzzy Control)、模糊粒子群控制器(Fuzzy-PSO Control)兩種模式來操縱Arduino微控制器進行溫度、濕度輸出的參數比較與調控。 在本研究所設定之五種不同情境目標值試驗中,濕度控制方面,模糊粒子控制器達標性能皆較模糊控制器優異;溫度控制方面,在溫度和濕度皆下降的情境下,模糊粒子控制溫度則因熱泵長時間啟動,且必須再開啟鰭片式加熱器增溫,過程接近取樣時間點,導致溫度狀態不平穩,因此模糊粒子控制器之溫度控制有較大誤差產生。除前項試驗溫控之結果外,綜合評比所有試驗情境,模糊粒子控制器達標性能皆優於模糊控制器,能更精準有效地達成目標濕度及目標溫度控制,溫度平均總誤差範圍皆在1℃以內,濕度平均總誤差範圍皆在5%以內。依玫瑰成長環境的適當日溫為21-29℃,夜溫為15-18℃,相對濕度為50-60%,模糊粒子控制器符合微環境控制之實務需求。
The establishment of microenvironment is flourished recently. Since the microenvironment is a hot topic for academia and industry, many efforts and resources have been dedicated to realization all over the world. A perfect created and controlled microenvironment not only can reconstruct a new farming model but also improve productivity and capability. The problems about labor-lack and influence from the global climate change can also be minimized with such systems simultaneously. This thesis will employ MATLAB codes, a Arduino micro-controller and the fuzzy particle swarm optimization theory algorithm to establish a micro-environment control scheme in which a NDIR CO2 meter will detect the microenvironment status inside the plant growth chamber. Then, status signs are conducted into the user interface that was coded by MATLAB as the input reference. It will be a convenient real-time surveillance system for user. The Fuzzy theory and PSO (Particle swarm optimization) algorithm compose the Fuzzy-PSO controller with which the microenvironment parameters including the temperature and humidity inside the plant growth chamber will be implemented and defined. To verify the effectiveness, five different scenarios were set to manipulate. In humidity control, the Fuzzy-PSO controller was superior to the Fuzzy controller. In temperature control, for the scenario of temperature and humidity decreased at the same time, the performance of Fuzzy-PSO controller was not as good as using Fuzzy controller only. After the heat pump worked for a relative long time, the heater had to be turned on to increase temperature. As this procedure worked near sampling time, it would resulted in the unstable status and a distinct errors. Except for the scenario above, all results conducted by the Fuzzy-PSO controller were more excellent than the Fuzzy one on temperature control. As the range of the ideal environment for roses grow is 21 to 29℃ during the daytime, 15 to 18℃ at night and the range of humidity is 50 to 60%. This thesis validates the effectiveness and practicality of the Fuzzy-PSO controller in temperature and humidity for micro-environmental control since temperature errors were controlled within 1℃, and humidity errors were controlled within 5%.
URI: http://hdl.handle.net/11455/97806
文章公開時間: 2021-08-23
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