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arXiv · 2206.00354

Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach

Bingzhuo Zhong·Majid Zamani·Marco Caccamo
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Abstract

In this paper, we provide a direct data-driven approach to synthesize safety controllers for unknown linear systems affected by unknown-but-bounded disturbances, in which identifying the unknown model is not required. First, we propose a notion of $\gamma$-robust safety invariant ($\gamma$-RSI) sets and their associated state-feedback controllers, which can be applied to enforce invariance properties. Then, we formulate a data-driven computation of these sets in terms of convex optimization problems with linear matrix inequalities (LMI) as constraints, which can be solved based on a finite number of data collected from a single input-state trajectory of the system. To show the effectiveness of the proposed approach, we apply our results to a 4-dimensional inverted pendulum.

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Bingzhuo Zhong, Majid Zamani, Marco Caccamo. 2022-06-01. Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach. https://doi.org/10.1109/ccta49430.2022.9966144

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