Greenhouse modeling and simulation framework for extracting optimal control parameters

In a greenhouse system, a control is important to allow optimal growth conditions for crops. However, because testing the greenhouse for real conditions requires much time and money, the modeling-and-simulation approach is necessary to predict and improve the greenhouse environment. There is much research related to greenhouse control, there is a lack of research on applicable frameworks for real greenhouses. Therefore, this paper proposes a greenhouse modeling-and-simulation framework to extract optimal control parameters. The proposed work is composed of three parts: system identification, controller design, and optimization. The plant model is built through system identification, and the model is controlled by the controller, which is affected by disturbances. This simulation is repeated through design of experiments to optimize the control parameters. This paper presents an experiment with real greenhouse data from Jinju, Korea to show the usefulness of the proposed framework. It gives insight into the decision of choosing control parameters and helps to raise agricultural productivity.