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| Volume 74, Issue 1
March 2024
DOI: 10.47978/TUS.2024.74.01
Table of Contents
Proposal and Study of Model Predictive Control System for Automated Greenhouse Management |
Ilker Yahov, Roumen Trifonov |
Abstract: This research explores the possibilities for application of Model Predictive Control (MPC) in greenhouse management to enhance climate precision and energy efficiency. Greenhouses play a crucial role in global food production, but maintaining ideal growing conditions is resource intensive. The study proposes an MPC strategy, empirically validated in a dynamic greenhouse environment, demonstrating its superiority in minimizing energy costs and achieving optimal resource consumption. Emphasizing alignment with Industry 4.0 principles, the research integrates MPC into modern agricultural practices, contributing to low energy consumption and reduced water and pesticide use. An experimental model simulates a commercial growth chamber, providing a platform for comprehensive testing under various scenarios. Despite inherent limitations, the model allows rigorous evaluation of different strategies, highlighting improved temperature control and energy efficiency. The study outlines innovative principles, emphasizing advantages such as intuitiveness, applicability to diverse processes, and robust constraint handling. Challenges, including accurate process modelling, are acknowledged. The findings promise to help revolutionizing greenhouse management, advancing the industry toward a more sustainable and technologically advanced future.
Keywords: Energy Efficiency, Climate Optimization, Model Predictive Control (MPC), Greenhouse Management |
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DOI: 10.47978/TUS.2024.74.01.001 |
Last changed on 24.03.2024, 20:24:15
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