Data-driven discovery of mode switching conditions to create hybrid models of cyber-physical systems

Models are essential tools for evaluating a system’s behaviour under different scenarios. However, in industrial practice pre-existing models of cyber-physical systems (CPSs) are not always available because CPSs can be legacy systems which are subject to changes and upgrades over time that may not be well documented. System identification addresses the problem by creating models from the external observation of a system. This research is concerned with hybrid system identification of CPSs, i.e., building models of dynamic systems switching between different operating modes. This thesis presents methods for discovering data-driven mode switching conditions essential for building such models