Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system

Atia, Doaa M.; El-Madany, Hanaa T.

doi:10.1016/j.jesit.2016.10.014

Cited by 70 publications

(46 citation statements)

References 15 publications

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“…In fact our approach is applicable both to the prediction of a single parameter and to that of n parameters. In addition, the correlation between those variables has already been demonstrated in some previous studies [44]. Moreover, whilst some adaptive neuro-fuzzy methods [45,46] produce target temperature as an output, the present approach produces two parameters that allow a rapid and more effective adjustment of microclimate control: air inlet speed (hot or cold) and its temperature, defined during design phase.…”

Section: Methodsmentioning

confidence: 58%

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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Abstract:In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production. This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system's capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process. A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable.

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Section: Methodsmentioning

confidence: 58%

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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“…Neuro-fuzzy can approximate certain types of nonlinear functions well in nature. Therefore, neuro-fuzzy models have been applied in designing control systems, such as the temperature control system for greenhouse [33], an antilock braking system of motor vehicle [34], a water-level control of Utube steam generators in nuclear power plants [35], and so on [3,7,8]. This study have exhibited that proposed methods have better properties than the conventional counter methods in function approximations and realworld benchmark problems.…”

Section: Discussionmentioning

confidence: 99%

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

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Abstract-The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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“…The plants play a strategic role in water and heat balance, thanks to the evapotranspiration process [10]; •…”

Section: Greenhouse Modelmentioning

confidence: 99%

“…Genetic algorithms implemented in a control system for irrigation in a greenhouse were proposed in References [7,8], while Reference [9] presented a comparative study of two types of fuzzy multivariate controllers to show their advantages and disadvantages. Reference [10] developed four control techniques to adjust the air temperature inside a greenhouse to a desired value: fuzzy logic control (FLC), an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network control (ANNC), and IP Control. ANFIS [11] and FLC [12] are two of the best known and most used controllers for nonlinear and complex processes such as greenhouses.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

et al. 2020

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An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.

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Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system

Cited by 70 publications

References 15 publications

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

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