Learning-Enabled Robust Control with Noisy Measurements

Olle Kjellqvist; Anders Rantzer

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arXiv · 2202.08363

Learning-Enabled Robust Control with Noisy Measurements

Olle Kjellqvist·Anders Rantzer

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Abstract

We present a constructive approach to bounded $\ell_2$-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of $\mathcal H_\infty$-observers paired with a recursively computed performance metric. We do not assume prior knowledge of a stabilizing controller.

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Olle Kjellqvist, Anders Rantzer. 2022-02-16. Learning-Enabled Robust Control with Noisy Measurements. https://arxiv.org/abs/2202.08363

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