Design of an Automatic Control Algorithm for Energy-Efficient ...

6 The optimiser: an evolutionary algorithm approach 50
states from a desired value. and represent the restrictions on the control and
the system states, also called boundaries or constraints (for varying limits).
Usually this is used to offline generate optimal reference trajectories employed as
feed-forward control. An approach to use optimal control in online applications in a closed
loop is called model predictive control (MPC) [30]. Here the starting time is set to
the actual time . The end time is set to a time . This is also called receding
horizon control. In the controller the system and its (usually piece-wise constant) control
output is simulated and optimised for this horizon . Then, the first output is taken
and applied to the system.
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Figure 6.1: The functioning of (simplified) model predictive control
For the case of the climate control some assumptions and simplifications to this
general formulation are made. For the objectives described in Chapter 2 the only true
trajectory cost would be energy consumption. Here the simplification is made that it
does not matter at which time the energy is used or the system is heated. This is not
completely valid (as described in Section 2.3), but situations like recuperating happen in
a much larger timescale than the horizon and can as well not be predicted. Moreover, the
heat transfer to the interior components changes depending on the air flow. This could
however be implemented in the thermal model.
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