Chance-constrained stochastic MPC of greenhouse production systems with parametric uncertainty

Greenhouse climate control is important to provide sufficient fresh food for the growing population in an economical and sustainable manner. However, the developed crop-climate models are generally complex with parametric uncertainties and far from describing the real system accurately, which affects adversely the control system’s performance. To improve optimality and guarantee robustness during the control process, we develop and implement a stochastic model predictive control (MPC) scheme for greenhouse production systems considering parametric uncertainties. By leveraging the advantages of model linearization, the proposed chance-constrained MPC method enables a more straightforward formulation of uncertainty constraints and computationally cheaper optimization in comparison to directly using the nonlinear model. Finally, the efficacy of the proposed approach is demonstrated on a greenhouse climate control case study.