Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation
Suitable environmental conditions are a fundamental issue in greenhouse crop growth and can be achieved by advanced climate control strategies. In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem fa...
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| Veröffentlicht in: | Control engineering practice 2011-04, Vol.19 (4), p.354-366 |
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| creator | Gruber, J.K. Guzmán, J.L. Rodríguez, F. Bordons, C. Berenguel, M. Sánchez, J.A. |
| description | Suitable environmental conditions are a fundamental issue in greenhouse crop growth and can be achieved by advanced climate control strategies. In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for dynamical reasons, leads to natural ventilation as a standard tool. This work addresses the design of a nonlinear model predictive control (NMPC) strategy for greenhouse temperature control using natural ventilation. The NMPC strategy is based on a second-order Volterra series model identified from experimental input/output data of a greenhouse. These models, representing the simple and logical extension of convolution models, can be used to approximate the nonlinear dynamic effect of the ventilation and other environmental conditions on the greenhouse temperature. The developed NMPC is applied to a greenhouse and the control performance of the proposed strategy will be illustrated by means of experimental results.
► Second-order Volterra model describing the inside greenhouse temperature. ► Nonlinear predictive controller to control the diurnal greenhouse temperature. ► Adequate results for different agricultural seasons (spring and autumn). |
| doi_str_mv | 10.1016/j.conengprac.2010.12.004 |
| format | Article |
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► Second-order Volterra model describing the inside greenhouse temperature. ► Nonlinear predictive controller to control the diurnal greenhouse temperature. ► Adequate results for different agricultural seasons (spring and autumn).</description><subject>Agronomy. Soil science and plant productions</subject><subject>Air pollution</subject><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Climate</subject><subject>Computer science; control theory; systems</subject><subject>Control system synthesis</subject><subject>Control theory. Systems</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Greenhouse climate control</subject><subject>Greenhouses</subject><subject>Identification</subject><subject>Mathematical models</subject><subject>Modelling and identification</subject><subject>Nonlinear model predictive control</subject><subject>Nonlinearity</subject><subject>Optimal control</subject><subject>Protected cultivation</subject><subject>Soilless cultures. 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In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for dynamical reasons, leads to natural ventilation as a standard tool. This work addresses the design of a nonlinear model predictive control (NMPC) strategy for greenhouse temperature control using natural ventilation. The NMPC strategy is based on a second-order Volterra series model identified from experimental input/output data of a greenhouse. These models, representing the simple and logical extension of convolution models, can be used to approximate the nonlinear dynamic effect of the ventilation and other environmental conditions on the greenhouse temperature. The developed NMPC is applied to a greenhouse and the control performance of the proposed strategy will be illustrated by means of experimental results.
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| subjects | Agronomy. Soil science and plant productions Air pollution Applied sciences Biological and medical sciences Climate Computer science control theory systems Control system synthesis Control theory. Systems Exact sciences and technology Fundamental and applied biological sciences. Psychology General agronomy. Plant production Greenhouse climate control Greenhouses Identification Mathematical models Modelling and identification Nonlinear model predictive control Nonlinearity Optimal control Protected cultivation Soilless cultures. Protected cultivation Strategy Temperature control Ventilation Volterra series model |
| title | Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation |
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