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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.04330 |
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| _version_ | 1866909890175303680 |
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| author | Portilla, Christian Aribowo, Arviandy G Anantharaman, Ramachandran Gómez-Pérez, César A Özkan, Leyla |
| author_facet | Portilla, Christian Aribowo, Arviandy G Anantharaman, Ramachandran Gómez-Pérez, César A Özkan, Leyla |
| contents | 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 (BDM) Funete et al.[2024], with light intensity signals -- comprising DC (static) and AC (modulated) components as input and chlorophyll fluorescence (ChlF) as output. Using these data, the Best Linear Approximation (BLA) method is employed to estimate second-order linear time-invariant (LTI) 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 (LPV) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Modeling of Photosynthesis Regulation Under Oscillating Light Condition - Part I: In-Silico Exploration Portilla, Christian Aribowo, Arviandy G Anantharaman, Ramachandran Gómez-Pérez, César A Özkan, Leyla Systems and Control 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 (BDM) Funete et al.[2024], with light intensity signals -- comprising DC (static) and AC (modulated) components as input and chlorophyll fluorescence (ChlF) as output. Using these data, the Best Linear Approximation (BLA) method is employed to estimate second-order linear time-invariant (LTI) 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 (LPV) 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. |
| title | Data-Driven Modeling of Photosynthesis Regulation Under Oscillating Light Condition - Part I: In-Silico Exploration |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.04330 |