Advanced Constrained Model Predictive Control of Vapor Pressure Deficit in Agricultural Greenhouses

The agricultural greenhouse is an open system to the external environment. The challenge is to track the reference trajectory of relevant climatic parameters and regulate the internal climate, despite the strong meteorological disturbances. This study focuses on the modelling and control of Vapor Pressure Deficit (VPD) coupled with temperature and hygrometry in greenhouses. The importance of this parameter is related to plant growth and its sensitivity to external climate disturbances. The VPD is a primordial parameter for preventing plant diseases caused by an inappropriate interior climate. This work provides a methodology for efficient control of VPD with a Multi-setting parameterization of the controller according to the needs of the plant and the actuators’ power available in the greenhouse. For this purpose, the VPD is indirectly evaluated through the ambient air temperature and humidity using the psychometric chart embedded in a fuzzy inference algorithm. For dynamic modelling of VPD, a discrete state-space model is obtained by considering an agricultural greenhouse as a black-box system. Numerical algorithms for Subspace State Space System Identification (N4SID) of Matlab is applied to parametric identification based on experimental measurements. A Constrained Model Predictive Control (CMPC) method is applied for VPD control design, considering physical and operational constraints on Input/output variables. Here, the CMPC strategy is improved by information on the greenhouse’s outside climatic conditions. Promising numerical simulation results show the feasibility of the proposed modelling and control strategy. They show good performances, despite the climatic exchange phenomenon between inside and outside the greenhouse and strong external weather disturbances. The inside VPD tracks nearly reference trajectories with a small overshooting as desired.