CAJ | 학술논문

针对传统温室控制系统中存在的控制方案达不到最优化、反应滞后、控制器调节不同步等问题,提出了基于全局变量预测模型的温室环境控制方法.该方法将温室内部温度、湿度、光照等数据,控制器当前状态,温室外部环境的相应数据及当地天气情况进行融合,利用各个全局变量通过数学模型得出温室未来环境状况的短期预测值,通过神经网络实现控制方案,解决了温室控制中的大滞后、大惯性等问题.实验结果证明了该方法的有效性及合理性,并对温室内气候智能控制的发展具有一定的参考价值.
침대전통온실공제계통중존재적공제방안체불도최우화、반응체후、공제기조절불동보등문제,제출료기우전국변량예측모형적온실배경공제방법.해방법장온실내부온도、습도、광조등수거,공제기당전상태,온실외부배경적상응수거급당지천기정황진행융합,이용각개전국변량통과수학모형득출온실미래배경상황적단기예측치,통과신경망락실현공제방안,해결료온실공제중적대체후、대관성등문제.실험결과증명료해방법적유효성급합이성,병대온실내기후지능공제적발전구유일정적삼고개치.
@@@@Greenhouse control system needs to control the actuators to make corresponding regulations according to the change of the greenhouse climate. When the temperature is too low, the heating system will be used to heat the greenhouse;when it is too high, the ventilation facility, the sun-shading system, or the spray equipment will be employed to cool the greenhouse and avoid overheat. In most conventional greenhouse control systems, the actuators were individually controlled based on the measured value and the setting value. This kind of control systems were working in passive mode and only made regulations when the greenhouse’s climate changed. It could not predict the future status of the greenhouse and then make regulations in advance. Besides, the actuators were established and set individually and could not work together harmoniously, which resulted in over-regulations and vibrations. Therefore, the control system needs to be developed with more intelligence for the whole system management. In this study, interior and exterior environmental information of the greenhouse, crop growing period and local climate data were integrated by using the global prediction model for the development of an innovative greenhouse control system. Compared to conventional greenhouse control systems, the interior and exterior temperature, the humidity, the ray radiation, the status of each actuator and near-future local climate were considered as global variable. The BP neutral networking was employed for model prediction. The global variable obtained from the corresponding sensors were input to the model to obtain the predicted values and the control system made the regulations with the use of PID before the climate changed. @@@@In order to validate the model, the experiment was conducted in a greenhouse for area of 96 m2, Because of the coupling effects of the various parameters, the greenhouse was divided into 5 areas: heating system, crop growing region, greenhouse side windows, ceiling and outside the greenhouse. Sensors were installed in each region, the data is collected, a total of 21 temperature sensors, 16 humidity sensors and two light sensors to be used .Prediction model of the BP neutral network consisted of three layers:the input layer, the hidden layer, and the output layer. Input parameters is the data collected by sensors, the state of six actuators and a weather forecast value, 4 prediction values are output:temperature, humidity, ray radiation, concentration of CO2. @@@@Tomato at growth stage of florescence was planted in the experiment greenhouse. The optimum temperature range for tomato at florescence period is 20-25 , the night temperature is 15℃ -20 , the optimum humidity range℃is 65%-85%. The experimental results showed that this model can be controlled greenhouse environment in the state of optimal crop growth environment. In order to further validate of the model, the PID control simulation results were used to compare the actual situation. Results showed that temperature and humidity changes in greenhouse with the prediction model were gentler than that with only the PID controller. That meant this method increased the stability of greenhouse environment control system. This study demonstrated that the model could avoid the lagging response, passive control and inharmonious regulation in conventional control systems and it was effective and rational.