Constrained Predictive Control of a Greenhouse

Piñón, Sandra; Camacho, Eduardo F.; Kuchen, B.

doi:10.3182/20020721-6-es-1901.01318

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…In this case, both solar radiation and wind speed forecasts would be necessary since both of them have a great influence on the greenhouse temperature control problem [5], [6]. When predictive control techniques have been used to control some of the processes described above, the future behavior of the disturbances has been considered as constant along the prediction horizon [7], [8]. Therefore, the availability of at least approximated future estimations could considerably improve the control results.…”

Section: Introductionmentioning

confidence: 98%

Application of time-series methods to disturbance estimation in predictive control problems

Pawłowski

Guzmán

Rodríguez

et al. 2010

2010 IEEE International Symposium on Industrial Electronics

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Disturbance effect is a very important issue to be analyzed and considered in any control problem. Models representing the disturbance effect on the process variable are usually obtained for this purpose. These models are useful to design, for instance, classical feedforward compensators. However, when predictive control strategies are being used, no only the model capturing the disturbance effect is necessary, but that future estimations of the disturbances would be also extremely useful to get advance in the prediction capabilities. Measurable disturbances can be represented as time-series data and processed in order to estimate future variations. Therefore, this paper is focused on studying how well-known time-series methods can be used to obtain disturbance forecasts in order to be taken into account in predictive control algorithms. The methods used here have two main objectives: to detect the nature and properties of any variable represented as time-series data, and second, to forecast future behavior based on acquired data series. An example using the solar radiation measured from an industrial greenhouse is presented to show how these future estimations can be obtained.

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Section: Introductionmentioning

confidence: 98%

Application of time-series methods to disturbance estimation in predictive control problems

Pawłowski

Guzmán

Rodríguez

et al. 2010

2010 IEEE International Symposium on Industrial Electronics

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“…In recent years, many approaches have been proposed to address the greenhouse climate control problem. [1][2][3][4][5][6][7] Various control theories, including logical control, 8 fuzzy control, 9 predictive control, 10 neural network (NN) control, 11 PID control, 12 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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“…State reconstructors have been proposed e.g. in the 1998 reference quoted in Piñón et al (2005), and in Speetjens, Stigter, and Van Straten (2009).…”

Section: State or Output Feedback Multivariable Controlmentioning

confidence: 99%

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Straten

Henten

2010

IFAC Proceedings Volumes

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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.

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Constrained Predictive Control of a Greenhouse

Cited by 9 publications

References 2 publications

Application of time-series methods to disturbance estimation in predictive control problems

Application of time-series methods to disturbance estimation in predictive control problems

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

Optimal Greenhouse Cultivation Control: Survey and Perspectives

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