Optimized Model Predictive Control for Solar-Assisted Earth-Air Heat Exchanger System in Greenhouse

Agricultural greenhouses play a crucial role in addressing the threat of climate change to agricultural systems. The greenhouse systems control exhibits nonlinear characteristics and large lag, both affecting the regulation of the greenhouse thermal environment. In this paper, a control strategy was designed to set the dynamic reference value of the control objective of the greenhouse model, considering factors such as solar radiation and the heating capacity of water body. A complete greenhouse model was developed using TRNSYS, and then the model predictive control building and optimization were implemented through MATLAB. The control effects of traditional proportional-integral-differentiation control, initial model predictive control, and optimized model predictive control were compared. The results showed that the regulation of greenhouse temperature was improved by 10.6% in the coldest mouth compared to the conventional proportional-integral-differentiation control by the optimized model predictive control. During a typical cold month, the violation of constraints was reduced by 29.7% by dynamically setting the target of the greenhouse with the optimized model predictive control. The performance of greenhouse climate control is enhanced by the proposed optimized model predictive control method, ensuring a stable regulation of the crop growth environment.