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- W2994692741 abstract "Summary In this article, we focus on developing a neural‐network‐based critic learning strategy toward robust dynamic stabilization for a class of uncertain nonlinear systems. A type of general uncertainties involved both in the internal dynamics and in the input matrix is considered. An auxiliary system with actual action and auxiliary signal is constructed after dynamics decomposition and combination for the original plant. The reasonability of the control problem transformation from robust stabilization to optimal feedback design is also provided theoretically. After that, the adaptive critic learning method based on a neural network is established to derive the approximate optimal solution of the transformed control problem. The critic weight can be initialized to a zero vector, which apparently facilitates the learning process. Numerical simulation is finally presented to illustrate the effectiveness of the critic learning approach for neural robust stabilization." @default.
- W2994692741 created "2019-12-26" @default.
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- W2994692741 date "2019-12-18" @default.
- W2994692741 modified "2023-10-06" @default.
- W2994692741 title "Neural critic learning toward robust dynamic stabilization" @default.
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- W2994692741 doi "https://doi.org/10.1002/rnc.4860" @default.
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