Reinforcement learning-based model predictive control for greenhouse climate control
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, predictio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003551 |
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| author | Samuel Mallick Filippo Airaldi Azita Dabiri Congcong Sun Bart De Schutter |
| author_facet | Samuel Mallick Filippo Airaldi Azita Dabiri Congcong Sun Bart De Schutter |
| author_sort | Samuel Mallick |
| collection | DOAJ |
| description | Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth. |
| format | Article |
| id | doaj-art-c416f1f4c7fb4de4a18ec6c8f13af9e3 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-c416f1f4c7fb4de4a18ec6c8f13af9e32025-08-20T02:52:20ZengElsevierSmart Agricultural Technology2772-37552025-03-011010075110.1016/j.atech.2024.100751Reinforcement learning-based model predictive control for greenhouse climate controlSamuel Mallick0Filippo Airaldi1Azita Dabiri2Congcong Sun3Bart De Schutter4Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, the Netherlands; Corresponding author.Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, the NetherlandsDelft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, the NetherlandsAgricultural Biosystems Engineering Group, Wageningen University, 6700 AA, Wageningen, the NetherlandsDelft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, the NetherlandsGreenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth.http://www.sciencedirect.com/science/article/pii/S2772375524003551Greenhouse climate controlModel predictive controlReinforcement learning |
| spellingShingle | Samuel Mallick Filippo Airaldi Azita Dabiri Congcong Sun Bart De Schutter Reinforcement learning-based model predictive control for greenhouse climate control Smart Agricultural Technology Greenhouse climate control Model predictive control Reinforcement learning |
| title | Reinforcement learning-based model predictive control for greenhouse climate control |
| title_full | Reinforcement learning-based model predictive control for greenhouse climate control |
| title_fullStr | Reinforcement learning-based model predictive control for greenhouse climate control |
| title_full_unstemmed | Reinforcement learning-based model predictive control for greenhouse climate control |
| title_short | Reinforcement learning-based model predictive control for greenhouse climate control |
| title_sort | reinforcement learning based model predictive control for greenhouse climate control |
| topic | Greenhouse climate control Model predictive control Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003551 |
| work_keys_str_mv | AT samuelmallick reinforcementlearningbasedmodelpredictivecontrolforgreenhouseclimatecontrol AT filippoairaldi reinforcementlearningbasedmodelpredictivecontrolforgreenhouseclimatecontrol AT azitadabiri reinforcementlearningbasedmodelpredictivecontrolforgreenhouseclimatecontrol AT congcongsun reinforcementlearningbasedmodelpredictivecontrolforgreenhouseclimatecontrol AT bartdeschutter reinforcementlearningbasedmodelpredictivecontrolforgreenhouseclimatecontrol |