Agriculture Reference
In-Depth Information
direct flow rate measurement used in Europe (Berckmans et al., 1991; Berckmans
and Vranken, 2006).
Discrete proportional control is typically favored for animal housing because
of its simplicity and flexibility (Chao and Gates, 1996; Chao et al., 1995, 2000).
Guidelines for operation of these systems are developed empirically (Chao et al.,
1995) and based on perceived performance benefits that are unique to the target
production system. This approach is limited as data describing the interactions of
modern genotypes with the surrounding environment are limited.
Frequency domain control design methods have been applied to animal housing
controls using traditional control theory such as proportional-integral-derivative
(PID) (MacDonald et al., 1989; van't Klooster et al., 1995), proportional-integral-plus
(PIP) (Taylor et al., 2004; Stables and Taylor, 2006), and robust control (Soldatos et
al., 2005). Axial fan control using traditional PID methods to compensate for pressure
fluctuations exhibited significant instability when operated outside the design range
(Vranken et al., 2005). Using fixed gain values for each controller action can lead to
instability, and adjusting gains for different ranges of system operation (gain schedul-
ing) can limit instability (Taylor et al., 2004). Gain scheduling allows for improved
performance of PID control loops applied to nonlinear processes (Aström et al., 1993),
but requires more extensive knowledge of process behavior over several operating
points when compared to typical implementations of PID control (Segovia et al., 2004).
Approaches such as artificial intelligence and model-based control have also been
used to design or improve animal housing control systems and operation. In particu-
lar, fuzzy inference systems appeared to hold promise to provide intuitive control
responses that balance between conflicting goals such as energy efficiency and con-
trol precision (Hamrita, 2002). Gates et al. (1999) and Chao et al. (2000) developed
design criteria for fuzzy logic control of staged ventilation, and Chao et al. (2000)
implemented a fuzzy logic controller for heating and ventilation system control that
offers the ability to select operational states based on needs for energy conservation
while maintaining acceptable precision, or conversely, improve precision at the cost
of additional energy usage.
Model-based control uses a predefined model of system behavior as a “target”
and seeks to match predicted performance, rather than react to and minimize dis-
turbances to the system. As noted previously, full mechanistic specification of the
dynamic model of animal housing systems is complex. Data-based models use avail-
able information about system behavior, and models are developed through sys-
tem identification methods (van Straten and van Willigenburg, 2006). Data-based
modeling approaches for animal housing control design have primarily addressed
improvement in ventilation rate control (Vranken et al., 2005; Price et al., 1999) or
temperature distribution control (Desta et al., 2005; Van Brecht et al., 2005).
Implementation of classical or advanced control in animal housing systems
remains limited. Focus on improving control methodologies in animal production
systems has tended toward improving the ability to specify operation setpoints to
improve productivity, efficiency of production, and minimize costs for economic
benefit to the producer. Indeed, the concept of an integrated management system
is of continuing interest as competing demands on the producer are conflicting in
many cases (Frost et al., 1997; Wathes et al., 2001). The integrated management
