In vertical plant factories, dynamic light management tailored to crop developmental stages is essential yet unattainable through conventional fixed-lighting approaches. This study presents an integrated control framework termed NMPC-PAR-LSTM, which synergizes three functional modules: a Michaelis-Menten kinetics model that generates growth-stage-specific photosynthetic targets, an LSTM network that forecasts environmental variations over a 20-step-ahead horizon, and an NMPC optimizer incorporating physiological constraints. Comparative evaluation demonstrates that the NMPC-PAR-LSTM system achieves a cumulative tracking error of 136 μmol·m −2 ·s −1 , representing reductions of 91.6% relative to Linear MPC (1610.6), 90.6% relative to LSTM-MPC (1451), and 45.2% relative to MPC-PAR-LSTM (248). The framework also exhibits superior control stability, with an input variation rate of only 4.2%, compared to 26.0% for Linear MPC and 88.3% for LSTM-MPC. By aligning supplemental lighting with the physiological demands of crops, this approach provides a viable pathway toward sustainable vertical agriculture.
Wang et al. (2026) studied this question.