Microclimate control of a greenhouse by adaptive Generalized Linear Quadratic strategy

Essahafi, Mohamed; Lafkih, Mustapha Ait

doi:10.11591/ijeecs.v11.i1.pp377-385

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The WSN's sensed data are processed and then used as multi-inputs to drive advanced control algorithms-based artificial intelligence to manage intelligently the greenhouse internal microclimate and to optimize the use of water and energy. These controllers can also vary from simple, such as feedback controllers (Mekki et al 2015;Maher et al 2016;Pawlowski et al 2017), to modern ones such as fuzzy logic controllers (Rahmawati et al 2018;Alpay and Erdem 2018;Xu et al 2016;Pahuja et al 2015;Márquez-Vera et al 2016;Chen et al 2016b;Wang and Zhang 2018;Li et al 2017bLi et al , 2018Ben Ali et al 2018;Faouzi et al 2017;Carrasquilla-Batista and Chacon-Rodrıguez 2017), artificial neural networks (Nicolosi et al 2017;Huang et al 2018;Francik and Kurpaska 2020;Singh 2017;Hongkang et al 2018;Moon et al 2018;Taki et al 2016), genetic algorithms (Wang et al 2017a;Mahdavian and Wattanapongsakorn 2017), model predictive controllers (Ouammi et al 2020b;Liang et al 2018b;Hamza and Ramdani 2020), sliding controllers (Khelifa et al 2020;Oubehar, et al 2016), adaptative controllers (Essahafi and Lafkih 2018), frequency controllers (Bagheri Sanjareh et al 2021), and receding horizon controllers based on prioritized multi-operational ranges (Singhal et al 2020).…”

Section: Full Automation Technologiesmentioning

confidence: 99%

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Karanisa

Achour²,

Ouammi

et al. 2022

Environ Syst Decis

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Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.

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Section: Full Automation Technologiesmentioning

confidence: 99%

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Karanisa

Achour²,

Ouammi

et al. 2022

Environ Syst Decis

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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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“…The detailed above discussion is a stimulus to our present work. Modelling and controlling of multivariable systems is very tedious because they are highly interactive and complex [11]. Often complex models are beneficial for the analysis of system behavior whereas simple models are generally used for design of feedback controllers [12].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of model predictive controller for an identified model of MIMO process

Saibabu

Sai

Yadav³

et al. 2020

IJEECS

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Model Predictive Controller (MPC) technology has been researched and developed to meet varied demands of need to control industrial power plants and petroleum refineries. This development has paved the way for the MPC technology too many other fields like automotive, aerospace, food processing industries in this paper, primary importance has been paid to the development of a MPC for an identified model of Multiple Input and Multiple Output process. In this paper, a Four Tank System has been considered for generation of input-output data. This data i.e. generated input output data is used for the estimation of two polynomial model, name ARX model (Autoregressive exogenous) model and OE (Output Error) model. With each of model output generated, the Fit-Rates of models are compared to find out most efficient model. The model equations are now considered as plant for developing a Model Predictive Controller (MPC). Two sets of results are obtained after the development of MPC and tested. One is without noise and one is with noise. Both sets of results were a success as the output signals traces step input signals after some steady oscillations in real time with in a very short period of time which indicated a good response time. The MPC developed can be applied to any polynomial model with a good Fit-Rate, it predicts and control the process variables automatically. Keywords:Auto regressive exogenous (ARX) Model predictive controller (MPC) Output error (OE)

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Microclimate control of a greenhouse by adaptive Generalized Linear Quadratic strategy

Cited by 5 publications

References 9 publications

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

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

Synthesis of model predictive controller for an identified model of MIMO process

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