Constrained Predictive Control of a Greenhouse

Piñón, Sandra; Peña, Miguel; Schugurensky, C.; Kuchen, B.

doi:10.1016/s1474-6670(17)40905-0

Cited by 16 publications

(20 citation statements)

References 1 publication

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“…Usually because this type of models offers a closer interpretation of phenomena (Baille et al, 1994(Baille et al, , 1996. This work exploits this alternative and in this section shows how a non-linear state space model is obtained from first principles, other alternative approaches can be found at (Coelho et al, 2005;Piñón et al, 2005).…”

Section: Greenhouse Climate Modelmentioning

confidence: 99%

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

Blasco

Martínez

Herrero

et al. 2007

Computers and Electronics in Agriculture

139

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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 nonlinear 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.

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Section: Greenhouse Climate Modelmentioning

confidence: 99%

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

Blasco

Martínez

Herrero

et al. 2007

Computers and Electronics in Agriculture

139

View full text Add to dashboard Cite

show abstract

“…In recent years, many approaches have been proposed to address the greenhouse climate control problem . Various control theories, including logical control, fuzzy control, predictive control, neural network (NN) control, PID control, etc, have been introduced to solve this problem. However, most control approaches in the literature focus mainly on the setpoint tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

Goodman

2017

Optim Control Appl Methods

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Summary Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming. In this method, 3 neural networks are used to estimate the value function and control policy and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an overactuated system, the total control efforts for heat, fog, and CO2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the neural networks to ensure the asymptotic convergence of the closed‐loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.

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“…Previous research applied nonlinear MPC to greenhouses temperature control , considering set points dynamically generated (thus varying the operation points) and strongly dependent on weather conditions, in such a way that the crop development and quality are affected as less as possible. Feedback linearization and MPC were combined in and shown to be restricted to a class of feedback linearized systems. Although the former techniques may provide some robustness to parameter variations and disturbances, this feature is often associated with a significant control signal increase and an oscillatory recovery.…”

Section: Introductionmentioning

confidence: 99%

A feasibility study of sliding mode predictive control for greenhouses

Oliveira

Boaventura‐Cunha

Oliveira

2015

Optim Control Appl Methods

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Summary In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright © 2015 John Wiley & Sons, Ltd.

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Constrained Predictive Control of a Greenhouse

Cited by 16 publications

References 1 publication

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

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

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

A feasibility study of sliding mode predictive control for greenhouses

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