A covariance-bound procedure yields a high-probability uncertainty set for nonlinear system parameters under unbounded stochastic noise, with convergence guarantees and direct use in probabilistic robust control.
On the sam ple complexity of the linear quadratic regulator
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Beyond Bounded Noise: Stochastic Set-Membership Estimation for Nonlinear Systems
A covariance-bound procedure yields a high-probability uncertainty set for nonlinear system parameters under unbounded stochastic noise, with convergence guarantees and direct use in probabilistic robust control.