Greenhouses play a critical role in the world's food production by enabling controlled crop growth in diverse and often suboptimal climates. Effective climate control is essential to maximise plant growth with minimal energy and resource usage. Natural ventilation, regulated through mechanical vents, is a key component of this control. To achieve consistent and efficient control, automation was introduced. Initially, greenhouse automation began with integrating RBC into climate computers. RBC provided a simple and structured approach to greenhouse climate control, making it easy for growers to operate the climate control system. In current practice, RBC remains widely used for the greenhouse natural ventilation control. However, over time, the accumulation of additional rules and settings has resulted in complex systems that are increasingly difficult to manage. The high number of settings and rules led to inconsistent and inefficient use by growers.

This thesis proposes a NMPC approach for greenhouse ventilation control as an alternative to the complex rules from the RBC structure. The study focuses on maintaining near-optimal greenhouse climate conditions while minimising mechanical wear and tear and reducing operational control complexity. Moreover, the thesis provides a detailed analysis of the RBC rules structure and settings of the MultiMa, a commercial climate control computer.

A simplified nonlinear greenhouse model is used to estimate the greenhouse dynamics. The model parameters are estimated with seasonal weighted offline NLLS using historical data of a commercial greenhouse. The seasonal parameter estimation creates a summer and winter model. The summer model provides an accurate estimation of the greenhouse dynamics. The modified winter model has less accurate performance, but still follows the overall trends of the measured temperature and humidity.

The models are used to simulate various NMPC approaches and compare them to the RBC system. The performance of the simulations is evaluated based on reference tracking accuracy and the total number of vent position changes, representing the ability to maintain climate conditions and minimise the wear and tear of the vents. The results of the simulation show that NMPC can achieve similar climate conditions to measured RBC climate conditions taken as reference, while significantly reducing the wear and tear. Moreover, NMPC can successfully integrate dynamic constraints, based on the current RBC constraint rules, into the optimisation.

Overall, the proposed NMPC framework presents promising results as an alternative to the complex rules and settings of the traditional control system. The \ac{NMPC} approach offers a more manageable and interpretable control system, while maintaining desired climate conditions in the greenhouse and minimising mechanical wear and tear.