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- W4287865720 abstract "A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach rests on two main ingredients. First, an optimal control formulation involving an ensemble of trajectories with 'control' variables given by the feedback gain functions. Second, an approximation to the feedback functions via realizations of neural networks. Based on universal approximation properties we prove the existence and convergence of optimal stabilizing neural network feedback controllers." @default.
- W4287865720 created "2022-07-26" @default.
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- W4287865720 date "2020-02-20" @default.
- W4287865720 modified "2023-10-16" @default.
- W4287865720 title "Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation" @default.
- W4287865720 doi "https://doi.org/10.48550/arxiv.2002.08625" @default.
- W4287865720 hasPublicationYear "2020" @default.
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