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Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control by Jung, Dae-Hyun; Kim, Hak-Jin; Kim, Joon Yong; Lee, Taek Sung; Park, Soo Hyun is a scholarly article available to read on EtoBox.

What is Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control about?

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R^2^ of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic a

Author

Jung, Dae-Hyun; Kim, Hak-Jin; Kim, Joon Yong; Lee, Taek Sung; Park, Soo Hyun

Publisher

Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2001-; Publons (ISSN 1424-8220)

Published

2020

Language

EN