Autonomous greenhouse control moves beyond static heuristic PID setpoints. By combining 24-hour weather forecasts with physiological crop models (Farquhar photosynthesis and Penman-Monteith transpiration), Model Predictive Control (MPC) and Deep Reinforcement Learning optimize pipe rail temperatures and vent openings, reducing natural gas usage by $22.4\%$ while maximizing harvest biomass.
1. The MPC Cost Objective Function
\min_{u} J = \sum_{k=0}^{N-1} \left( w_{energy} \cdot P_{heat}(k) - w_{yield} \cdot A_{net}(k) + w_{\Delta u} \|\Delta u(k)\|^2 \right)