Data-driven analysis and control of continuous-time systems under aperiodic sampling

We investigate stability analysis and controller design of unknown continuous-time systems under state-feedback with aperiodic sampling, using only noisy data but no model knowledge. first derive a novel data-dependent parametrization all linear time-invariant which are consistent the measured assumed noise bound. Based on this by combining tools from robust control theory time-delay approach to sampled-data control, we compute lower bounds maximum sampling interval (MSI) for closed-loop given gain, beyond that, controllers exhibit possibly large MSI. Our methods guarantee properties robustly data. As technical contribution, proposed embeds existing into general framework, can be directly extended model-based uncertain uncertainty descriptions.