A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
A note on persistency of excitation,
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Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.
Adversaries can poison data-driven observability analysis by applying invertible linear transformations to data matrices to embed malicious states and destroy informativity certificates.
citing papers explorer
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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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Data-Driven Reachability Analysis with Optimal Input Design
Row-norm-minimizing right inverse via SOCP plus A-optimal input design within the constrained matrix zonotope framework reduces conservatism in data-driven reachable sets for linear and piecewise-affine systems.
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Data Poisoning Attacks on Informativity for Observability: Invariance-Based Synthesis
Adversaries can poison data-driven observability analysis by applying invertible linear transformations to data matrices to embed malicious states and destroy informativity certificates.