RISS 검색 - 국내학술지논문 상세보기

다국어 초록 (Multilingual Abstract)

The energy saving performance of the greenhouse production is significantly impacted by the setpoint of the greenhouse climate and the control method. How to select a good setpoint for the greenhouse climate is an important issue. To solve this issue, this work proposes a decision support strategy to generate online the setpoint for the control of the greenhouse climate. In this approach, it uses online receding horizon multi-objective optimization to maximize the crop yield and minimize the energy consumption. Thus, it can obtain the optimal daily mean temperature of each day. Since such method does not directly optimize the sepoint of the greenhouse climate, it must introduce the daily mean temperature serialization method to transform the daily mean temperature into the setpoint curve. Once the sepoint is generated, the next task is to solve the control problem of the greenhouse climate. Since the greenhouse climate is a complex nonlinear system, and is impacted by the greenhouse structure and material, the weather and the crop growth. Therefore, it is usually difficult to accurately model the greenhouse climate. The great uncertainty of the system makes the control problem of the greenhouse climate be difficult to solve. To solve this problem, this work proposes an adaptive hybrid control based on a greenhouse climate model with unknown timevariant parameters. In this control method, neural network is used to estimate the model parameters. Based on such a model, an adaptive control law is derived to generate the control inputs of the heating, fogging and CO2 injecting, while the control strategies of the ventilation, shading and thermal screen are determined by the expert rules. The simulation results indicate that such adaptive hybrid control method can achieve good control performance and economic efficiency.

참고문헌 (Reference)

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