From Theory to Harvest: Robust MPC Supervising Smart Greenhouse System

Advanced control methods play a crucial role in making greenhouse agriculture more energy-efficient and sustainable. This paper introduces an offset-free tube model predictive control (MPC) framework for the control of temperature and humidity in the laboratory smart greenhouse VESNA. The proposed approach effectively handles parametric uncertainties while maintaining optimal internal conditions for plant growth. The effective reference tracking of temperature and disturbance rejection for humidity are provided by the implemented robust MPC method. An extensive experimental analysis investigates different control setups considering various penalty matrices. Control performance was analyzed by evaluating control criteria, including steady-state offset, total energy consumption, and associated carbon footprint. This study demonstrates the potential of robust MPC for sustainable greenhouse control by providing offset-free control while minimizing energy consumption and carbon emissions.