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

6 The optimiser: an evolutionary algorithm approach 51
With this change made, all objectives are only dependent on the states at the end
time, leading to the following formulation, which is visualised in Figure 6.1.
��� ����� � � ��������� ��� � � ���������
���� ��� ���� �� ��� ��
��� � � �� �� � � � � � �������
��� � � �� �� � � � � � �������
���� �� �
(6.2)
Since the cost is only dependent on values at the end time the control signal can be
kept constant during the simulated time � �������. Therefore, only one time step has to be
simulated and optimised each run. This reduces the computational effort dramatically,
since only one set-point and not an optimal trajectory has to be found.
6.2 Algorithm selection
In this problem formulation a numerical method is still needed to solve the (nonlinear)
optimisation problem in each time step.
6.2.1 Selection criteria
The algorithm should fulfil certain criteria to be able to be used for a car climate con-
trol. Robustness is important since the car parameters will never be exactly known. A
minimum number of (as cheap as possible) sensors is desired as well, which reduces the
data quality. In all those cases the algorithm has to provide reasonable solutions. Those
should of course never cause a danger to the passengers. This means that the control
has to respect limits and prioritise objectives in certain situations. For example the inlet
temperature should never be below zero degree at high inlet mass flows. In addition, the
windscreen has to be kept free from fog even if this causes a temporary conflict with other
objectives. To run on a system on a chip (SoC) in car at a reasonable frequency it has to
have a low computational effort. The time scale of several seconds is relatively long for
a control problem, but not that long for an optimisation task, especially as the objective