Multiscale fuzzy model-based short term predictive control of greenhouse microclimate

Ramdani, Messaoud; Hamza, Azzedine; Boughamsa, Mouna

doi:10.1109/indin.2015.7281931

Cited by 10 publications

(17 citation statements)

References 11 publications

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“…More advanced controllers that require accurate mathematic was also applied to control greenhouse systems. Model Predictive Control was largely used in controlling greenhouse system proprieties, we refer the reader to [45][46][47][48]. Feedback and feedforward control was also applied to control greenhouse systems [49][50][51].…”

Section: Figure 5 Greenhouse Closed Loop Temperature Regulation Systemmentioning

confidence: 99%

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

2020

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The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

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Section: Figure 5 Greenhouse Closed Loop Temperature Regulation Systemmentioning

confidence: 99%

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

2020

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“…The parameters obtained by the identification procedure can be optimised or fine-tuned by a variant of gradient descent optimisation techniques (Ramdani et al, 2015).…”

Section: Parameter Adaptation Proceduresmentioning

confidence: 99%

“…The majority of the developed systems are only dedicated to temperature control. Nevertheless, the humidity has an important effect on crop productivity, authors like Yuquan et al (2010) proposed an intelligent control system based on a microcomputer, Montero et al (2011) developed an optimised climate control in different geographic areas, Ramdani et al (2015) presented a nonlinear predictive control scheme for temperature and humidity regulation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control strategy for greenhouse micro-climate

Boughamsa

Ramdani

2018

IJAAC

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This paper describes a model predictive controller design to regulate the greenhouse micro-climate, where the controller outputs are computed to optimise the future behaviour of the greenhouse's environment, concerning the setpoint accuracy of the internal temperature and humidity described by Takagi-Sugeno (T-S) model. Modelling procedure is based on two steps. First, the identification of the antecedent part where local linear models are valid using the well-known fuzzy C-means clustering algorithm. Then, recursive least squares (RLS) algorithm is used for consequent part parameters adaptation. An adaptive T-S fuzzy model is considered within the control scheme for prediction of the future greenhouse behaviour. The main way of controlling the greenhouse micro-climate is to use heating and ventilation to regulate both internal temperature and humidity. The simulation results show that the proposed approach maintains successfully both temperature and humidity within the greenhouse around the desired set points in the presence of disturbances. The simulation results are compared between MPC controller based on T-S fuzzy model and MPC based on a single linear model.

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“…Greenhouse cultivation then has the fundamental objective of protecting plantations from bad weather and diseases. It is a complex system that in recent years has become a means to achieve controlled agricultural production by providing a strictly controlled climate [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

Vanegas-Ayala

Barón-Velandia

Leal-Lara

2022

Advances in Human-Computer Interaction

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Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

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Multiscale fuzzy model-based short term predictive control of greenhouse microclimate

Cited by 10 publications

References 11 publications

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature

Adaptive fuzzy control strategy for greenhouse micro-climate

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

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