Sampling Performance of Periodic Event-Triggered Control Systems
a Data-driven Approach
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Abstract
We employ the scenario optimisation theory to compute a traffic abstraction, with probability guarantees of correctness, of a PETC system with unknown dynamics from a finite number of samples. To this end, we extend the scenario optimisation approach to multiclass SVM in order to compute a map between the concrete state space and the intersample times of the PETC. This map allows the construction of a traffic abstraction, through an <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula>-complete relation, that provides upper and lower bounds on the sampling performance of the concrete system. We further propose an alternative path to build such abstraction, first we identify the model and then apply a model-based procedure. Numerical benchmarks show the practical applicability of our methods for noiseless and noisy samples.