Application of model predictive control via crop dry weight model in greenhouse climate control

This study presents a model predictive control (MPC) system for greenhouse climate, whose input command is a predefined model of crop dry weight. Moreover, using the Box-Jenkins (BJ) method, a simulation based on data collected from a commercial greenhouse was presented to predict its climate. Based on this system, the effect of crop growth model changes on the response of the control system was calculated. The simulation system output was parameters of indoor air temperature, absolute humidity, CO2 concentration, and crop dry weight. The input parameters were CO2 supply rate, heating rate, and greenhouse ventilation rate. Based on this simulation system, the MPC controller was designed in such a way that the user can enter the desired set points or a predefined function. To show the effect of this function on climate quality and crop growth in the greenhouse, three input models of step, ramp, and polynomial were considered as reference commands to set the MPC control system. Furthermore, a PID controller was also used to show the efficiency of this control system and to compare its response.