A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
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2026 2verdicts
UNVERDICTED 2representative citing papers
The authors introduce an output-feedback CBF framework using expectation-based barrier conditions and Jensen inequality bounds to conservatively enforce safety in stochastic systems with partial observations.
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Data-Driven Probabilistic Finite $\mathcal{L}_2$-Gain Stabilization of Stochastic Linear Systems
A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
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Output-Feedback Safe Control of Discrete-Time Stochastic Systems with Chance Constraints
The authors introduce an output-feedback CBF framework using expectation-based barrier conditions and Jensen inequality bounds to conservatively enforce safety in stochastic systems with partial observations.