Data-driven discovery of mode switching conditions to create hybrid models of cyber-physical systems
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Mukhtar Hussain Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
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.
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| ID Code: | 235043 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Fidge, Colin, Ul Huque, Tanvir, Jadidi, Zahra, & Foo, Ernest |
| Keywords: | Cyber-Physical Systems, Hybrid Petri Nets, Guard Conditions, Hybrid System Identification, Decision Mining |
| DOI: | 10.5204/thesis.eprints.235043 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
| Institution: | Queensland University of Technology |
| Deposited On: | 09 Sep 2022 04:54 |
| Last Modified: | 09 Sep 2022 04:54 |
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