Feng Chen, Yong-Ning Tang and Ming-Yu Shen. Coordination Control of Greenhouse Environmental Factors. International Journal of Automation and Computing, vol. 8, no. 2, pp. 147-153, 2011. DOI: 10.1007/s11633-011-0567-3
Citation: Feng Chen, Yong-Ning Tang and Ming-Yu Shen. Coordination Control of Greenhouse Environmental Factors. International Journal of Automation and Computing, vol. 8, no. 2, pp. 147-153, 2011. DOI: 10.1007/s11633-011-0567-3

Coordination Control of Greenhouse Environmental Factors

  • 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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