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- W4282004690 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." @default.
- W4282004690 created "2022-06-13" @default.
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- W4282004690 date "2022-06-01" @default.
- W4282004690 modified "2023-09-29" @default.
- W4282004690 title "Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach" @default.
- W4282004690 doi "https://doi.org/10.48550/arxiv.2206.00354" @default.
- W4282004690 hasPublicationYear "2022" @default.
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