Data-driven input-to-state stabilization with respect to measurement errors

Abstract

We consider noisy input/state data collected from an experiment on a polynomial input-affine nonlinear system. Motivated by event-triggered control, we provide data-based conditions for input-to-state stability with respect to measurement errors. Such conditions, which take into account all dynamics consistent with data, lead to the design of a feedback controller, an ISS Lyapunov function, and comparison functions ensuring ISS with respect to measurement errors. When solved alternately for two subsets of the decision variables, these conditions become a convex sum-of-squares program. Feasibility of the program is illustrated with a numerical example.

arXiv commentSubmitted for peer review on 31 March 2023. To appear in the Proceedings of the 62nd IEEE Conference on Decision and Control, 13-15 December 2023, Singapore

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