13.11.5 Controllers
The advantages of using optimal instead of conventional greenhouse climate
control can be summarized as follows. An optimal control approach to greenhouse
climate control fully exploits scientific quantitative knowledge concerning the
greenhouse atmosphere, the soil, the equipment, the crop, and their interactions.
All of them captured in a mathematical dynamic model that deals with the problem
of maximizing the profit, achieving welfare of the crop through practices that
minimizes production costs (Van Straten et al. 2010).
Robust controllers were applied in protected agriculture because its ability to
deal with uncertain parameters, disturbances, or modeling mistakes (Linker et al.
2011). They were applied focusing on managing the high correlation between air
temperature and hygrometry (Bennis et al. 2008). This decoupling allows the use
of two control loops, one for each variable. Despite the modeling uncertainties and
strong disturbances, the controller presented an acceptable performance.
Horticultural research has indicated that for the majority of plants, crop growth
responds to long-term average temperatures rather than specific day and night
temperature profiles. This principle is used in the well-known temperature integration technique (TI). Where is possible to adjust the setpoint temperature in a
flexible way to obtain a desired average temperature instead at fixed value over the
time. Therefore, energy savings could be obtained decreasing flexibility to the
heating set points when conditions are favorable and lowering it when they are not.
This knowledge was applied by Sigrimis et al. (2000) in the design of a tool
available to exploit the interaction between photosynthesis and growth according
to empiric knowledge. The method is based on varying heating set points using
previously recorded information in order to achieve the desired average for any
user-defined period. Meanwhile, (Körner and Challa 2003) decoupled process with
fast temperature response (e.g., photosynthesis or stress) from a process with a
slow response time. The objective was to improve the temperature integration
concept by introducing dynamic temperature constrains; these flexible boundaries
depend on the underlying crop process while increasing the potential for energy
saving in greenhouses. Despite the promising energy savings obtained with temperature integration, the potential of this technique is limited by humidity if usual
set points are maintained, because the high relation between those variables
counteracts the TI. A promising solution is the use of a process-based humidity
regimen. In this regimen, relative humidity set points can freely move within a
range, avoiding affect the TI objectives. However, as humidity changes could
highly affect the crop quality and yield, the set points its duration period were
calculated in order to avoid plant-affecting processes such as Ca-deficiency, plant
water stress, crop growth, crop development and airborne fungal diseases (Körner
and Challa 2004).
Another way to improved TI in cold lands was addressed by (Aaslyng et al.
2003), which used mathematical models for estimating the absorption of irradiance, leaf photosynthesis and respiration. Then, the temperature which was
392
M. S. Acosta-Navarrete et al.
The advantages of using optimal instead of conventional greenhouse climate
control can be summarized as follows. An optimal control approach to greenhouse
climate control fully exploits scientific quantitative knowledge concerning the
greenhouse atmosphere, the soil, the equipment, the crop, and their interactions.
All of them captured in a mathematical dynamic model that deals with the problem
of maximizing the profit, achieving welfare of the crop through practices that
minimizes production costs (Van Straten et al. 2010).
Robust controllers were applied in protected agriculture because its ability to
deal with uncertain parameters, disturbances, or modeling mistakes (Linker et al.
2011). They were applied focusing on managing the high correlation between air
temperature and hygrometry (Bennis et al. 2008). This decoupling allows the use
of two control loops, one for each variable. Despite the modeling uncertainties and
strong disturbances, the controller presented an acceptable performance.
Horticultural research has indicated that for the majority of plants, crop growth
responds to long-term average temperatures rather than specific day and night
temperature profiles. This principle is used in the well-known temperature integration technique (TI). Where is possible to adjust the setpoint temperature in a
flexible way to obtain a desired average temperature instead at fixed value over the
time. Therefore, energy savings could be obtained decreasing flexibility to the
heating set points when conditions are favorable and lowering it when they are not.
This knowledge was applied by Sigrimis et al. (2000) in the design of a tool
available to exploit the interaction between photosynthesis and growth according
to empiric knowledge. The method is based on varying heating set points using
previously recorded information in order to achieve the desired average for any
user-defined period. Meanwhile, (Körner and Challa 2003) decoupled process with
fast temperature response (e.g., photosynthesis or stress) from a process with a
slow response time. The objective was to improve the temperature integration
concept by introducing dynamic temperature constrains; these flexible boundaries
depend on the underlying crop process while increasing the potential for energy
saving in greenhouses. Despite the promising energy savings obtained with temperature integration, the potential of this technique is limited by humidity if usual
set points are maintained, because the high relation between those variables
counteracts the TI. A promising solution is the use of a process-based humidity
regimen. In this regimen, relative humidity set points can freely move within a
range, avoiding affect the TI objectives. However, as humidity changes could
highly affect the crop quality and yield, the set points its duration period were
calculated in order to avoid plant-affecting processes such as Ca-deficiency, plant
water stress, crop growth, crop development and airborne fungal diseases (Körner
and Challa 2004).
Another way to improved TI in cold lands was addressed by (Aaslyng et al.
2003), which used mathematical models for estimating the absorption of irradiance, leaf photosynthesis and respiration. Then, the temperature which was
392
M. S. Acosta-Navarrete et al.