Data-driven modelling of photosynthesis regulation under oscillating light condition: in-silico exploration

Abstract This paper explores the application of data-driven system identification techniques in the frequency domain to obtain simplified, control-oriented models of photosynthesis regulation under oscillating light conditions. In-silico datasets are generated using simulations of the physics-based Basic DREAM Model Fuente et al. (A mathematical model to simulate the dynamics of photosynthetic light reactions under harmonically oscillating light. Plant Physiol Biochem 2024;217:109138.), with light intensity signals—comprising DC (static) and AC (modulated) components as input and chlorophyll fluorescence as output. Using these data, the best linear approximation method is employed to estimate second-order linear time-invariant transfer function models across different operating conditions defined by DC levels and modulation frequencies of light intensity. Building on these local models, a linear parameter-varying representation is constructed, in which the scheduling parameter is defined by the DC values of the light intensity, providing a compact state-space representation of the system dynamics.