Intelligent Climate Control System inside a Greenhouse

Labidi, A; Chouchaine, Amine; Mami, Abdelkader

doi:10.14569/ijacsa.2021.0120229

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In late many years, numerous scientists have centered on modeling and monitoring the greenhouse environment. Diverse technics are used such as multi-model and neural modeling [3], [4], fuzzy control [5], [6] optimal control [7], [8] robust control [9]. The non-efficiency of classical methods has forced researchers looking forward advanced methods namely neural networks or more specially artificial intelligence (AI) Int J Elec & Comp Eng ISSN: 2088-8708  Linear matrix inequalities tool to design predictive model control for greenhouse climate (Ayoub Moufid)…”

Section: Introductionmentioning

confidence: 99%

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Moufid

Boutchich²,

Bennis³

2023

IJECE

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<span lang="EN-US">Modeling and regulating the internal climate of a greenhouse have been a challenge as it is a complex and time variant system. The main goal is to regulate the internal climate considering the difference between nighttime and diurnal phases of the day. To depict the comportment of the greenhouse, a multi model approach based on two multivariable black box models have been proposed representing the diurnal and nighttime phases of the day. The least-squares method is utilized to identify the parameters of these two models based on an experimental collected data. We have shown that these two models are more representative than one model to describe the dynamic behavior of the greenhouse. The second contribution is to control the internal temperature and hygrometry respecting constraints on actuators and controlled variables. For this purpose, a constrained model predictive control scheme based on the multi-modeling approach have been developed. The optimization problem of the control law is transformed to a convex optimization problem includes linear matrix inequalities (LMI). The simulation results show that the adopted control method of indoor climate allows rapid and precise tracking of set points and rejects effectively the external disturbances affecting the greenhouse.</span>

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Section: Introductionmentioning

confidence: 99%

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Moufid

Boutchich²,

Bennis³

2023

IJECE

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Automated Crop Growth Monitoring and Optimizing the Yield with Reinforcement Learning

Gorantla,

Veluchamy

2023

2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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Artificial House for Swiftlets (COLLOCALIA FUCIPHAGA) Based on MAMDANI FIS (Fuzzy Inference System)

Marzuki,

Heryawan,

Dulhan

2024

AJECE

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Indonesia is an archipelagic country crossed by the equator, surrounded by the ocean, and traversed by many rivers, with a high level of rainfall. One of its islands is West Kalimantan, which has the longest river in Indonesia, the Kapuas River. In addition to the Kapuas River, there are also several tributaries scattered across this island, ensuring a constant water supply in the region. A natural condition surrounded by water, creates an environment with temperature and humidity that is ideal for the habitat of the swiftlets (COLLOCALIA FUCIPHAGA). The swiftlets initially inhabit caves with humidity levels around 80-95% and temperatures between 25-27°C. The nests of these swiftlets contain valuable substances, especially for health and cosmetics purposes, making them highly valuable in the market. Due to their high economic value, many farmers construct artificial houses for swiftlets of various sizes. The main challenge in a swiftlet house is to maintain humidity and temperature conditions close to the ideal habitat. This research aims to create a prototype control and monitoring system based on Mamdani FIS to maintain humidity and temperature inside the swiftlets house close to the ideal conditions, achieved by implementing an effective timing mechanism for humidifier machines and fans. Experiments results of the prototype show that the average humidity produced is 87.06%, and the temperature is 25.19°C. The placement of this prototype within an Artificial house for swiftlets should be movable within certain rooms and positioned according to the swiftlets' needs to ensure that the location is favored by the birds. This prototype can be operated manually and automatically, thus providing flexibility for Swiftlet farmers to control and monitor the Swiftlet’s house condition. This automatic control can be done globally via smartphone devices, so it is also able to provide great convenience for Swiftlets farmers to change and monitor the temperature and humidity.

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Intelligent Climate Control System inside a Greenhouse

Cited by 3 publications

References 20 publications

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Automated Crop Growth Monitoring and Optimizing the Yield with Reinforcement Learning

Artificial House for Swiftlets (COLLOCALIA FUCIPHAGA) Based on MAMDANI FIS (Fuzzy Inference System)

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