Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review

A major objective in the design and operation of buildings is to maintain occupant comfort without incurring significant energy use. Particularly in narrower-plan buildings, the thermophysical properties and behaviour of their façades are often an important determinant of internal conditions. Building facades have been, and are being, developed to adapt their heat and mass transfer characteristics to changes in weather conditions, number of occupants and occupant’s requirements and preferences. Both the wall and window elements of a facade can be engineered to (i) harness solar energy for photovoltaic electricity generation, heating, inducing ventilation and daylighting (ii) provide varying levels of thermal insulation and (iii) store energy. As an adaptive façade may need to provide each attribute to differing extents at particular times, achieving their optimal performance requires effective control. This paper reviews key aspects of current and emerging adaptive façade technologies. These include (i) mechanisms and technologies used to regulate heat and mass transfer flows, daylight, electricity and heat generation (ii) effectiveness and responsiveness of adaptive façades, (iii) appropriate control algorithms for adaptive facades and (iv) sensor information required for façade adaptations to maintain desired occupants’ comfort levels while minimising the energy use

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Model-based predictive control of greenhouse climate for reducing energy and water consumption

[EN] This work focuses on development of control algorithms by incorporating energy and water consumption to maintain climatic conditions in greenhouse. Advanced control algorithms can supply solutions to modern exploitations. The new developments usually require accurate models (probably multivariable and non-linear ones) and control methodologies capable of using these models. As an additional requirement it is important for the final application to be easy to use, so advanced control will not mean an increase in complexity of the manipulation of the installation. This article shows an alternative to classical climate control. It is based on two fundamental elements: an accurate non-linear model and a model-based predictive control (MBPC) that incorporate energy and water consumption. Genetic algorithms (GAs) play a key role in these two elements because functions to solve are non-convex and with local minima. First of all GAs supply a way to adjust the non-linear model parameters obtained from first principles, and finally GAs open the possibility of using non-linear model in the MBPC and of establishing a flexible cost index to minimize energy and water consumption. The results on a plastic greenhouse with arch-shaped roofs and for Mediterranean area are presented, important reduction in energy and water used in the cooling system (nebulization) is obtained. (c) 2006 Elsevier B.V. All rights reserved.Partially supported by MEC (Spanish government) and FEDER funds: projects DPI2004-8383-C03-02 and DPI2005-07835.Blasco, X.; Martínez Iranzo, MA.; Herrero Durá, JM.; Ramos Fernández, C.; Sanchís Saez, J. (2007). Model-based predictive control of greenhouse climate for reducing energy and water consumption. Computers and Electronics in Agriculture. 55(1):49-70. https://doi.org/10.1016/j.compag.2006.12.001S497055

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production

A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system

Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing

Branch and Bound algorithms in greenhouse climate control

The horticultural sector has become an increasingly important sector of food production, for which greenhouse climate control plays a vital role in improving its sustainability. One of the methods to control the greenhouse climate is Model Predictive Control, which can be optimized through a branch and bound algorithm. The application of the algorithm in literature is examined and analyzed through small examples, and later extended to greenhouse climate simulation. A comparison is made of various alternative objective functions available in literature. Subsequently, a modidified version of the B&B algorithm is presented, which reduces the number of node evaluations required for optimization. Finally, three alternative algorithms are developed and compared to consider the optimization problem from a discrete to a continuous control space.Grant TIN2015-66680-c2-2-R from the Spanish state in part financed by the European Regional Development Fund (ERDF

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

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