A hybrid-controlled approach for maintaining nocturnal greenhouse temperature: Simulation study

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 (R2 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 and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.

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“…Many studies have designed ventilation control systems through models based on physical phenomena occurring within greenhouses [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Park

Kim

et al. 2020

Sensors

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“…A commercial greenhouse must create a suitable environment by regulating the greenhouse microclimate to increase crop growth, thereby allowing an increase in the income of the grower, so long as the associated costs are not excessive. In recent years, many approaches have been proposed to address the greenhouse climate control problem . Various control theories, including logical control, fuzzy control, predictive control, neural network (NN) control, PID control, etc, have been introduced to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many approaches have been proposed to address the greenhouse climate control problem. [1][2][3][4][5][6][7] Various control theories, including logical control, 8 fuzzy control, 9 predictive control, 10 neural network (NN) control, 11 PID control, 12 etc, have been introduced to solve this problem. However, most control approaches in the literature focus mainly on the setpoint tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

Goodman

2017

Optim Control Appl Methods

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Summary Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming. In this method, 3 neural networks are used to estimate the value function and control policy and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an overactuated system, the total control efforts for heat, fog, and CO2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the neural networks to ensure the asymptotic convergence of the closed‐loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.

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“…proposed idea about combining two heating system: forced-air heaters and pipe systems. These system are usually used individually due to of the complexity in control of the combined system, they proposed idea of combining the two heating system and control it by using a hybrid controller [4]. N. Sivakumaran et.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Temperature Control System for a Prototype Greenhouse

Shenan¹,

Marhoon²,

Jasim³

2017

IJCA

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Know days, the Greenhouse has been improved and developed by using modern technologies. The target of the paper is designing and implementing an intelligent cooling/heating system, the system should be built by employing a low power consumption and efficient device that called Thermo-electric cooler (Peltier). The main goal of this system is to make the temperature inside the greenhouse under control and provide the plants with appropriate environments to ensure healthy products. The system implementation should be divided into two parts: hardware and software. The hardware part is compromising the process of designing and implementing a control circuit that works on deriving the Peltier according control signals that come from the microcontroller. The second part (Software) is about designing a fuzzy controller that act as a brain of the system and manages the entire process until reaching the desired setpoints. The Fuzzy controller depend on two inputs the error (Err) and change of the error (ΔErr). The overall system is implemented based on a standalone microcontroller with a simple sensor and actuator. The prescribed system has been tested with several experiments using various transition set points. The experiments results show an accepted response that suitable for greenhouse applications.

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A hybrid-controlled approach for maintaining nocturnal greenhouse temperature: Simulation study

Cited by 43 publications

References 9 publications

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

An Intelligent Temperature Control System for a Prototype Greenhouse

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