Coordination control of greenhouse environmental factors |
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Authors: | Feng Chen Yong-Ning Tang Ming-Yu Shen |
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Affiliation: | [1]Department of Automation, University of Science and Technology of China, Hefei 230027, PRC [2]School of Information Technology, Illinois State University, Normal IL 61790, USA [3]School of Computer and Information Science, Hefei University of Technology, Hefei 230066, PRC |
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Abstract: | Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear
and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and
CO2 concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental
factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.
Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control
for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce
the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning
process. The experimental results demonstrate that this approach is practical, highly effective and efficient. |
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Keywords: | Q-learning case-based reasoning (CBR) greenhouse environmental factors coordination control coupled correlation trial-and-error |
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