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- 저자 : Yuanping Su (검색결과 2 건)
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Greenhouse Climate Fuzzy Adaptive Control Considering Energy Saving
Yuanping Su,Erik D. Goodman,Lihong Xu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4
This paper proposes a fuzzy adaptive control approach to solve greenhouse climate control problem. Theaim is to ensure the controlled environmental variables to track their desired trajectories so as to create a favorableenvironment for crop growth. In this method, a feedback linearization technique is first introduced to derive thecontrol laws of heating, fogging and CO2 injection, then to compensate for the saturation of the actuators, a fuzzylogic system (FLS) is used to approximate the differences between controller outputs and actuator outputs due toactuator constraints. A robust control term is introduced to eliminate the impact of external disturbances and modeluncertainty, and finally, Lyapunov stability analysis is performed to guarantee the convergence of the closed-loopsystem. Taking into account the fact that the crop is usually insensitive to the change of the environment insidethe greenhouse during a short time interval, a certain amount of tracking error of the environmental variables isusually acceptable, which means that the environmental variables need only be driven into the corresponding targetintervals. In this case, an energy-saving management mechanism is designed to reduce the energy consumption asmuch as possible. The simulation results illustrate the effectiveness of the proposed control scheme.
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Yuanping Su,Lihong Xu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.11
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.
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