Hierarchical model predictive control for managing agricultural greenhouse systems

This paper presents a hierarchical model predictive control method for the management of agricultural greenhouse systems. The proposed approach consists of an optimization layer and a control layer. At the optimization layer, an optimization strategy is proposed to minimize the total costs of greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO2) supply and carbon emissions while maintaining greenhouse environmental factors, including temperature, humidity and CO2 concentration, within specified ranges. The proposed method is compared with a baseline method that minimizes greenhouse operating costs. At the control layer, a model predictive controller (MPC) is designed to track the reference trajectory obtained from the optimization layer. Simulation results show that the proposed method can reduce the total cost by R827 and the carbon emissions by 1.16 tons compared with the baseline method. Moreover, the designed MPC controller is verified to have good control performance under different levels of system disturbances. The proposed method is helpful to realize cleaner production and sustainable development of agricultural greenhouses.