Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives.

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Model Predictive Control for Offset-Free Reference Tracking

Transactions on Electrical Engineering ◽

10.14311/tee.2016.1.008 ◽

2020 ◽

Vol 5 (1) ◽

pp. 8-13

Author(s):

Květoslav Belda

Keyword(s):

Model Predictive Control ◽

State Space ◽

Predictive Control ◽

Space Form ◽

Control Design ◽

Tracking Problem ◽

Font Size ◽

Reference Tracking ◽

Reference Signals ◽

Consecutive Time

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">The paper deals with the offset-free reference tracking problem of the Model Predictive Control (MPC). That problem is considered for a class of the constant or occasionally changed constant reference signals. Proposed solution arises from a simple subtraction of the ARX model <br /> of two consecutive time steps. The solution is adapted <br /> to a state-space form and it corresponds to usual predictive control design without increase of the design complexity. The construction of the prediction equations and predictive controller structure is explained in the paper.</span>

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Constrained Discrete Model Predictive Control of a Greenhouse Relative Humidity

E3S Web of Conferences ◽

10.1051/e3sconf/202122901001 ◽

2021 ◽

Vol 229 ◽

pp. 01001

Author(s):

Hafsa Hamidane ◽

Samira El Faiz ◽

Abdeslam Lachhab ◽

Mohammed Guerbaoui ◽

Abdelali Ed-Dahhak

Keyword(s):

Mathematical Model ◽

Relative Humidity ◽

Model Predictive Control ◽

Numerical Simulations ◽

State Space ◽

Predictive Control ◽

Discrete Model ◽

Space Form ◽

Optimization Procedure ◽

System Stability

In this paper, we present a Constrained Discete Model Predictive Control (CDMPC) strategy application for relative humidity control. In this sense, and for our system inside humidity dynamics description, a green-house prototype is engaged and a state space form which fits properly a set of collected data of the greenhouse humidity dynamics is presented as mathematical model. This latest is used for the CDMPC starategy application, which purpose is to select the best control moves based on an optimization procedure regarding the constraints on the control. By the means of Matlab/ Simulink and Yalmip toolbox algorithms, numerical simulations were held to proove the effectiveness of the controller, garanteeing both the constraints feasibility and system stability.

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