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Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
[article]
2022
arXiv
pre-print
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based control has the potential to yield superior performance in demanding applications, robustness to uncertainty remains an important challenge. Since Bayesian methods quantify uncertainty of the learning results, it is natural to incorporate these uncertainties into a robust design. In contrast to most state-of-the-art
arXiv:2105.07668v2
fatcat:4dt2wmwgyva2tgjfvvnpphv6ju