A data-driven method designs probabilistic finite L2-gain stabilizers for stochastic linear systems from noisy trajectories via LMIs.
The intrinsic state variabl e in fundamental lemma and its use in stability design for data-b ased control
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Dynamical systems have an intrinsic state variable that bijectively encodes their behavior in a causality-free way, allowing control design entirely in state space and a neural network to learn this representation from operating data.
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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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Behavioral Systems Theory Meets Machine Learning: Control-Aware Learning of the Intrinsic Behavior from Big Data
Dynamical systems have an intrinsic state variable that bijectively encodes their behavior in a causality-free way, allowing control design entirely in state space and a neural network to learn this representation from operating data.