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Coordination control of greenhouse environmental factors

  • Feng ChenEmail author
  • Yong-Ning Tang
  • Ming-Yu Shen
Article

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.

Keywords

Q-learning case-based reasoning (CBR) greenhouse environmental factors coordination control coupled correlation trial-and-error 

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiPRC
  2. 2.School of Information TechnologyIllinois State UniversityNormalUSA
  3. 3.School of Computer and Information ScienceHefei University of TechnologyHefeiPRC

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