Hybrid Model Predictive Control for the regulation of carbon dioxide in plant growth chambers

Precise regulation of carbon dioxide (CO2) concentrations in plant growth chambers is critical for ensuring reproducible and physiologically relevant research outcomes. CO2 assimilation varies significantly with plant genotype, growth conditions, crop density, and phenological stage. However, estimation and control approaches heavily dependent on mechanistic crop models are at odds with the objectives of plant characterization units (PCU), where model availability for specific crops may be lacking. Moreover, in Bioregenerative Life Support Systems (BLSSs), such methods may struggle with multiple crops, intercropping, staggered harvesting and unknown growth stages. We propose a real-time, crop-agnostic method to estimate photosynthetic and respiration rates from CO2 concentration data, without relying on crop-specific mechanistic assumptions. This improves robustness against the time-varying conditions typical of BLSSs, and supports operation with crops lacking validated physiological models. The resulting rate estimates support diagnostic algorithms, supervisory logic and CO2 concentration controllers, and provide the modeling foundation for our second contribution: a hybrid Model Predictive Control (MPC) strategy for CO2 regulation. The controller employs a mixed-integer formulation to handle the disjoint operating ranges of injection valves and incorporates explicit compensation for CO2 measurement delays, ensuring accurate mass balances under real operating conditions. We demonstrate the effectiveness of the approach through in vivo experiments in a PCU realized under the ESA-MELiSSA framework.