Comfort Control Techniques for the Users of a Room - Comfort Control in Buildings - page 150

Fig. 5.1 Classical feedback control scheme

• Performance Bounds (PB). The last one is not a control strategy which can be

implemented in a real way. More specifically, it is a concept which can be defined
as the optimal control actions using perfect predictions of time and internal gains,
and thus, it can provide the limits that any controller can reach. Therefore, it is
used as a theoretical reference point (Oldewurtel et al. 2010b ).

For each one of the previous control strategies diverse comparisons among them
have been performed (Gwerder et al. 2010 ; Gyalistras et al. 2010 ; Oldewurtel et al.
2010a , b ). One of themost relevant control strategies is the stochasticMPC developed
in Oldewurtel et al. ( 2010b ). This strategy takes into account weather predictions to
increase energy efficiency at the same time that the constraints which satisfy users'
comfort are reached. One of the most important characteristics of this strategy is
that it uses stochastic time-variant constraints, a numerical model of meteorological
prediction, a bilinear model of the thermal dynamics of the building and models
of the automatised sub-systems based on a multi-node network of resistances and
capacitances. The authors have performed simulations throughout a whole year with
a sample time equal to one hour. For example, in Gyalistras et al. ( 2010 ) a compar-
ison between energy saving which can be achieved with a Nonlinear Model-based
Predictive Control (NMPC) technique (1-15%) and the theoretical energy saving
that is obtained with an MPC strategy (16-41%) is made.

5.2 Theoretical Background

From a general point of view, automatic control can be defined as “ the combination
of a collection of techniques which are used to fulfil the specifications of a deter-
mined problem in an autonomous way ” (Guzmán 2006 ). In addition, one of the most
important topics within the framework of automatic control is the term feedback, see
Fig. 5.1 , which provides the way to correct the difference between the desired and
the actual output of the controlled system.
As can be observed in Fig. 5.1 , a classical feedback control system has two main
components, the process and the controller. The process receives as input the manip-
ulated variable, u , that is also known as the control variable. Moreover, the output of
the process is the controlled variable, y , and its value is usually obtained by means
of sensors. Furthermore, the desired value of the controlled variable, y , is called
setpoint or reference value, w . Finally, the control error, e , is the difference between
the setpoint and the controlled variable.