This dissertation provides efficient (data-driven) techniques to design highly-reliable stochastic hybrid systems with mathematical guarantees by bringing together interdisciplinary concepts from control theory, formal methods in computer science, and data science. In particular, my PhD dissertation develops different (compositional) approaches including (i) discretization-based techniques based on (in)finite abstractions, (ii) discretization-free techniques based on control barrier certificates, and (iii) model-free techniques based on data-driven control.
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This dissertation provides efficient (data-driven) techniques to design highly-reliable stochastic hybrid systems with mathematical guarantees by bringing together interdisciplinary concepts from control theory, formal methods in computer science, and data science. In particular, my PhD dissertation develops different (compositional) approaches including (i) discretization-based techniques based on (in)finite abstractions, (ii) discretization-free techniques based on control barrier certificates...
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