Reserarch On Modeleing And Control Strategy Of Solar Greenhouse

The microclimate environment of solar greenhouse is a nonlinear system with large time delay,which is strongly coupled and easily affected by the external environment;At the same time,In the microclimate environment variables,indoor temperature mainly affects the growth of crops,so temperature is the primary controlled object of greenhouse environment;In summer,the control equipment used in solar greenhouse is the most and the control situation is the most complex.Therefore,this paper mainly studies the indoor temperature control of solar greenhouse in summerFirst,aiming at the overall measurement of greenhouse temperature in large area and complex environment,an adaptive weighted fusion algorithm based on fusion matrix is proposed.It first removes the failed data from the measured values of multiple homogeneous temperature sensors deployed.On this basis,the remaining sensor data are fused to get the temperature value which can represent the whole environment of greenhouse.Secondly,the mechanism model of greenhouse temperature was established to determine the main factors affecting the indoor temperature.The temperature system is divided into four working modes according to the combination of equipment switches,and the working characteristics of each mode are determined.It provides the basis for making the transition rules of switching control state.At the same time,the autoregressive moving average model with input is used to complete the system identification under four modes,which provides a reference model for temperature prediction of predictive control.Thirdly,aiming at the optimal control of indoor temperature in solar greenhouses,by introducing the theory of accumulated temperature of crops,planning the temperature target range for control.On the basis of switching control,a temperature control method based on model prediction is proposed.Among them,the switching controller designs switching rules through the combination of different indoor and outdoor temperatures;Model predictive control takes temperature control error,equipment switching times,and system energy consumption as constraints to establish system performance index functions.The control problem is transformed into the problem of solving the minimum objective function value,and the local optimal switchgear control sequence is obtained after the solution,and the optimal temperature switching control state is entered through the rolling optimization mechanism.The experimental results show that the model predictive control accuracy is higher than the event-triggered switching control,which can effectively regulate the temperature in the greenhouse and reduce the energy consumption of the greenhouse system.Finally,the intelligent control system of solar greenhouse is designed,which consists of remote control end and field centralized control center.In the field centralized control center,the data is collected by wireless sensor network,and the monitoring software is developed based on Lab VIEW.The software includes data processing,model predictive control and other functional modules.The control command controls the action of rolling machine,fan and wet curtain through PLC to control the temperature.GRM500 module is used as the core of the remote control terminal,and the data network is used to remotely control PLC to operate the greenhouse through the smart phone.Continuous experiments show that the system is stable and reliable,which can effectively adjust the indoor temperature and reduce energy consumption.