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

Abstract

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. We first derive a novel data-dependent parametrization of all linear time-invariant continuous-time systems which are consistent with the measured data and the assumed noise bound. Based on this parametrization and by combining tools from robust control theory and the time-delay approach to sampled-data control, we compute lower bounds on the maximum sampling interval (MSI) for closed-loop stability under a given state-feedback gain, and beyond that, we design controllers which exhibit a possibly large MSI. Our methods guarantee the stability properties robustly for all systems consistent with the measured data. As a technical contribution, the proposed approach embeds existing methods for sampled-data control into a general robust control framework, which can be directly extended to model-based robust controller design for uncertain time-delay systems under general uncertainty descriptions.

Final version, accepted for presentation at the 19th IFAC Symposium on System Identification (SYSID), 2021. This version contains the full proofs of Theorems 4 and 5 as well as additional details regarding the verification of assumptions in the appendix

References in corpus (4)

• Combining Prior Knowledge and Data for Robust Controller Design
• Data-Driven Control for Linear Discrete-Time Delay Systems
• Trade-offs in learning controllers from noisy data
• Data-driven analysis and control of continuous-time systems under aperiodic sampling

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