Greenhouse climate control aims to improve crop productivity while reducing resource use such as heating, ventilation, $CO_2$ injection, and supplemental lighting. Model predictive control provides a systematic framework for optimizing these inputs, but its performance depends strongly on the crop model used inside the controller. Existing greenhouse crop models, such as the van Henten lettuce model, are suitable for optimization but generally do not include variables that use fluorescence parameters and therefore, cannot be used with a crop fluorescence sensor.

This thesis investigates how fluorescence-derived crop feedback can be incorporated into a greenhouse optimal control framework. A proposed assimilation model is developed using electron transport rate (ETR) and PSII redox state $q_L$, two variables obtained from chlorophyll fluorescence measurements. The resulting Differential algebraic equations are formulated so that it can be evaluated within a gradient-based optimization framework.

A sensitivity analysis is performed to determine how variations in environmental conditions and uncertain physiological parameters affect the predicted assimilation rate. The analysis shows that the influence of the model inputs and parameters depends on the prevailing environmental and physiological conditions.

The proposed assimilation model is subsequently incorporated into the van Henten lettuce growth model and ultimately used within an adaptive economic model predictive controller. The controller determines the greenhouse inputs that maximize crop production while minimizing resource consumption. Closed-loop simulations are performed under parameter mismatch, online parameter adaptation, and direct physiological-feedback configurations. Results of the simulations show that the new photosynthesis formulation can be used in a optimal control setting and that the adaptive formulation reduces the effect of parameters mismatch ... Read more