An integrated moving horizon estimation and model predictive control framework for semi-closed greenhouse systems

This paper focuses on developing an optimization-based nonlinear output feedback control framework for semi-closed greenhouse systems. The proposed control architecture integrates moving horizon estimation (MHE) and model predictive control (MPC) to optimize crop growth and the operating costs through regulating greenhouse environment properties at desired steady state conditions. MHE estimates the unmeasurable states and unknown parameters required by MPC using the feedback from the measurement sensors. A lumped multiscale model is developed to integrate the greenhouse microclimate dynamics with the plant growth dynamics, describing the impact of the environmental conditions and decision variables on crop development. The resulting model is then employed as the basis for the integrated nonlinear MHE and MPC design. The closed-loop performance of the proposed control architecture is also compared to a traditional linear MPC with a Kalman filter in the presence of time-varying environmental conditions and disturbances in the manipulated inputs and measured outputs.