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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Veröffentlicht in:	IEEE transactions on control systems technology Jg. 30; H. 3; S. 1186 - 1197
Hauptverfasser:	Chen, Wei-Han, You, Fengqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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