Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control

Ghoumari, M. Y. El; Tantau, H.-J.; Serrano, Javier

doi:10.1016/j.compag.2005.08.005

Cited by 101 publications

(31 citation statements)

References 1 publication

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“…Secondly, it may be non-convex and even multi-modal. Hence, a number of suboptimal MPC algorithms have been developed in which the neural model is linearised around the current operating point and the obtained linear approximation is next used for the optimisation of the future control policy (El Ghoumari and Tantau, 2005;Ławryńczuk, 2007;2008;2009a;2009b;Nørgaard et al, 2000;Tatjewski, 2007;Tatjewski and Ławryńczuk, 2006). Thanks to linearisation, the future control policy is found by means of an easily solvable quadratic programming problem.…”

Section: Mpc Algorithm With Nonlinear Prediction and Linearisation Bamentioning

confidence: 99%

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Nonlinear predictive control based on neural multi-models

Ławryńczuk¹,

Tatjewski²

2010

International Journal of Applied Mathematics and Computer Science

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

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Section: Mpc Algorithm With Nonlinear Prediction and Linearisation Bamentioning

confidence: 99%

“…(b) MPC in which the neural model is linearised on-line: the control policy is calculated by a quadratic programming routine, e.g., (El Ghoumari and Tantau, 2005;Ławryńczuk, 2007;Nørgaard et al, 2000;Tatjewski, 2007).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear predictive control based on neural multi-models

Ławryńczuk¹,

Tatjewski²

2010

International Journal of Applied Mathematics and Computer Science

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“…In control system, they are also controlled by the control components [2]. Therefore, the greenhouse system is a non-linear, multi-input multi-output (MIMO) complex system they present time-varying behaviors and they are subject to pertinent disturbances depending generally on meteorological conditions [3]. All these make it difficult to describe a greenhouse with analytic models and to control them with classical controllers.…”

Section: Introductionmentioning

confidence: 99%

A Greenhouse Control with Sectional-Control Strategy Based on MPT Intelligent Algorithm

Wang

Lin

Feng

et al. 2013

Computer and Computing Technologies in Agriculture VI

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Abstract. The greenhouse are classified as complex systems such as large lag, multi-input multi-output (MIMO), non-linear and difficult to create mathematic model and so on, so it is difficult to implement classical control methods for this kind of process. A new sectional-control strategy is put forward. It uses different control modes for different deviation domains. The new strategy is based on MPT intelligent control algorithm which improves on the traditional PID algorithm and adds in self-adapting, fuzzy control, expert self-tuning etc intelligent control functions. When the deviation exceeds a certain domain, fuzzy control is used to prevent the saturated integral; when the deviation reduces to within a certain domain, MPT algorithm is used to reduce the overshooting during response process and eventually eliminate the residual. The application in greenhouse control shows that the sectional-control strategy makes the output tracing the set value correctly.

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“…A greenhouse is an enclosed space that creates a different environment to that found outside due to the confinement of the air and to the absorption of shortwave solar radiation through a plastic or glass covers (El Ghoumari et al, 2005). This generates a new environment inside the greenhouse which is better known as microclimate.…”

Section: Introductionmentioning

confidence: 99%

“…The results were compared with a PID solution. MPC based controllers solve the problems commonly presented in PID systems (El Ghoumari et al, 2005). Due to the advantages presented by MPC, different strategies have been applied to design optimal MPC controllers.…”

Section: Optimal Controlmentioning

confidence: 99%

Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

Duarte-Galvan

Torres-Pacheco

Guevara-González

et al. 2012

Span. j. agric. res.

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Today agriculture is changing in response to the requirements of modern society, where ensuring food supply through practices such as water conservation, reduction of agrochemicals and the required planted surface, which guarantees high quality crops are in demand. Greenhouses have proven to be a reliable solution to achieve these goals; however, a greenhouse as a means for protected agriculture has the potential to lead to serious problems. The most of these are related to the inside greenhouse climate conditions where controlling the temperature and relative humidity (RH) are the main objectives of engineering. Achieving appropriate climate conditions to ensure high yield and quality crops reducing energy consumption have been the objective of investigations for some time. Different schemes in control theories have been applied in this field to solve the aforementioned problems. Therefore, the objective of this paper is to present a review of different control techniques applied in protected agriculture to manage greenhouse climate conditions, presenting advantages and disadvantages of developed control platforms in order to suggest a design methodology according to results obtained from different investigations.Additional key words: controller; conventional control; optimal control; precision agriculture; protected agriculture. ResumenRevisión. Ventajas y desventajas de los sistemas de control climático aplicados en agricultura de precisión Hoy en día la agricultura está cambiando de acuerdo a las necesidades de la nueva sociedad. Las nuevas tendencias son asegurar la producción de alimentos a través de prácticas tales como ahorro de agua, reducción en el uso de agroquímicos y el espacio requerido para sembrar los cultivos mientras se garantiza la alta calidad de los cultivos. Los invernaderos han demostrado ser una solución viable para garantizar estos objetivos. Sin embargo, el uso de un invernadero conlleva serios problemas. Los más importantes están relacionados con las condiciones del microclima dentro del invernadero, donde el objetivo de la ingeniería es controlar la temperatura y humedad relativa (RH). Alcanzar las condiciones adecuadas del microclima para garantizar la alta productividad y calidad de los cultivos mientras se reducen los consumos de energía ha sido el objetivo de diversos investigadores a través del tiempo. Diversos esquemas de teoría de control han sido aplicados con el objetivo de resolver los problemas antes mencionados. Por lo tanto, el objetivo de este artículo es presentar una revisión de las diferentes técnicas de control aplicadas en agricultura de precisión para manejar las condiciones del microclima del invernadero, presentando las ventajas y desventajas de los sistemas desarrollados con la finalidad de proponer una metodología de diseño de acuerdo a los resultados obtenidos de las diferentes investigaciones.

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Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control

Cited by 101 publications

References 1 publication

Nonlinear predictive control based on neural multi-models

Nonlinear predictive control based on neural multi-models

A Greenhouse Control with Sectional-Control Strategy Based on MPT Intelligent Algorithm

Review. Advantages and disadvantages of control theories applied in greenhouse climate control systems

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