In the calibration of modern combustion engines, stationary model-based approaches are common. For future developments, the use of dynamic data-based modeling is an important and required step to further increase the efficiency and quality of the calibration process. In order to achieve high modeling quality, these models require informative measurement data. This necessitates a transient variation of the input signals during the measurement process. At the same time the safety of the system under test needs to be ensured during the measurements by supervising critical output signals.
This thesis covers different methods to tackle this task, namely for stationary and dynamic design of experiments. Therefore, two strategies are presented and analyzed: on the one hand the use of an offline generated design of experiment, on the other hand the use of active learning. The first strategy is threefold. It starts with the identification of the stationary safe system boundary. Thereafter a dynamic design of experiment is created within the stationary safe boundary. Finally, a dynamic measurement is pursued, while the system under test is safeguarded by a newly introduced supervising controller. The second strategy uses a dynamic safe active learning approach. Thereby, the dynamic model is not anymore learned offline, after the whole measurement was completed, but online, in parallel to the measurement process. Instead of a predefined design of experiment, the queried trajectories are optimized iteratively based on the learned model. The optimization also considers dynamic safety constraints of the system under test.
Special emphasis is put on the application of these strategies to real-world measurements in the combustion engine domain. The various design of experiments, measurement, and active learning algorithms are compared and their specific advantages and disadvantages are discussed.