Semiclosed Greenhouse Climate Control Under Uncertainty via Machine Learning and Data-Driven Robust Model Predictive Control

This work proposes a novel data-driven robust model predictive control (DDRMPC) framework for automatic control of greenhouse in-door climate. The framework integrates dynamic control models of greenhouse temperature, humidity, and CO 2 concentration level with data-driven robust optimization models...

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Vydané v:IEEE transactions on control systems technology Ročník 30; číslo 3; s. 1186 - 1197
Hlavní autori: Chen, Wei-Han, You, Fengqi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Agriculture
Ambient temperature
Automatic control
Carbon dioxide
Carbon dioxide concentration
Climate models
Clustering
Controlled environment agriculture
data-driven robust optimization
Dynamic control
Green products
greenhouse climate control
Greenhouses
Humidity
Machine learning
Optimal control
Optimization models
Predictive control
Robust control
robust model predictive control (RMPC)
Solar radiation
Temperature distribution
Uncertainty
Weather forecasting
ISSN:1063-6536, 1558-0865
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Shrnutí:This work proposes a novel data-driven robust model predictive control (DDRMPC) framework for automatic control of greenhouse in-door climate. The framework integrates dynamic control models of greenhouse temperature, humidity, and CO 2 concentration level with data-driven robust optimization models that accurately and rigorously capture uncertainty in weather forecast error. Data-driven uncertainty sets for ambient temperature, solar radiation, and humidity are constructed from historical data by leveraging a machine learning approach, namely, support vector clustering with weighted generalized intersection kernel. A training-calibration procedure that tunes the size of uncertainty sets is implemented to ensure that data-driven uncertainty sets attain an appropriate performance guarantee. In order to solve the optimization problem in DDRMPC, an affine disturbance feedback policy is utilized to obtain tractable approximations of optimal control. A case study of controlling temperature, humidity, and CO 2 concentration of a semiclosed greenhouse in New York City is presented. The results show that the DDRMPC approach ends up with 14% and 4% lower total cost than rule-based control and robust model predictive control with L 1 -norm-based uncertainty set, respectively. The constraint violation probability, which is the percentage of time that the greenhouse system states violate the constraint throughout the whole growing period, for DDRMPC is only 0.39%. Hence, the proposed DDRMPC framework can prevent the greenhouse climate from becoming harmful to plants and fruits. In conclusion, the proposed DDRMPC approach can improve the greenhouse climate control performance and reduce cost compared with other control strategies.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2021.3094999