Adaptive predictive control of a fan-ventilated tunnel greenhouse with evaporative cooling

In the present work, micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling is investigated via simulation study. For this purpose, the adaptive generalized predictive control (AGPC) strategy is adopted to control temperature, relative humidity, and CO2 concentration in the greenhouse on extremely hot summer days. A dynamic model which integrates a distributed model of greenhouse micro-climate with a crop growth model is used for numerical simulation of the tunnel greenhouse. The controller performance is analyzed via numerical simulation. The relative gain array analysis confirms that greenhouse micro-climatic parameters are highly interactive. However, simulation results showed that AGPC can efficiently handle variables interaction and follow set-points despite actuator limitations and high outside temperature. Besides, by considering output constraints in applying AGPC, the control system can keep the humidity of the greenhouse in a specific range instead of in a particular set point. The calculation burden for the AGPC strategy is kept low without increasing prediction error by employing a linear model and on-line adaptive identification. The applied strategy can handle the uncertainty of the design model due to the model mismatch by on-line identification of model parameters using the recursive least square method.& COPY; 2023 Published by Elsevier Ltd.

Automated summary

In this study, the micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling was investigated using simulation. The adaptive generalized predictive control (AGPC) strategy was adopted to control the greenhouse's temperature, relative humidity, and CO2 concentration. The simulation results showed that AGPC efficiently handled variables interaction and could maintain set-points despite limitations and high outside temperature.