Abstract
Greenhouse climate is a multiple coupled variable, nonlinear and uncertain system. It consists of several major environmental factors, such as temperature, humidity, light intensity, and CO2 concentration. In this work, we propose a constraint optimal control approach for greenhouse climate. Instead of modeling greenhouse climate, Q-learning is introduced to search for optimal control strategy through trial-and-error interaction with the dynamic environment. The coupled relations among greenhouse environmental factors are handled by coordinating the different control actions. The reinforcement signal is designed with consideration of the control action costs. To decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm Case Based Reasoning (CBR) is seamlessly incorporated into Q-learning process of the optimal control. The experimental results show this approach is practical, highly effective and efficient.
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Chen, F., Tang, Y. (2010). Towards Constraint Optimal Control of Greenhouse Climate. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_52
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DOI: https://doi.org/10.1007/978-3-642-15615-1_52
Publisher Name: Springer, Berlin, Heidelberg
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