Abstract
M.Ing.
This research project is dedicated to the automation of environmental control within
greenhouses. To create an optimal climate in the greenhouse, the main environmental
parameters that need to be controlled are temperature, humidity and light intensity. As
a result of process dead times and the extreme interdependence of these parameters,
the control problem can be classified as non-linear and multi-variable.
In the past, most greenhouse environmental control systems depended on the decision
making of an experienced human operator. This often gave rise to trial and error,
especially when new species were established. With the current advances in
"intelligent" control systems and high accuracy sensors, more and more of the
decisions involved in greenhouse control can be automated. In this way more emphasis
can be placed on emulating the abilities of an expert operator, by means of a computerbased
automatic control system.
In this research project, "intelligent" as well as "non-intelligent" control techniques, for
addressing the problem of automated climate control in a greenhouse, are investigated.
These include PID-control as a "non-intelligent" technique, and rule-based fuzzy logic
control and self-learning fuzzy logic control as two "intelligent" control techniques.
These techniques are all applied to experimental greenhouse which is equipped with
management mechanisms, such as fans, heaters, sprinklers and lights.
The results of the experiments are evaluated according to two performance
parameters: the Control Performance Index (CPI) and the Mean Square Error (MSE).
The three techniques are not only assessed for their efficiency, but also for their
applicability to the greenhouse environmental problem. Each of the control techniques
has a unique characteristic response to the non-linear, non-stationary, multi-variable
problem of environmental control and are subsequently addressed in the respective
chapter.