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Neural Stochastic Contraction Metrics for Robust Control and Estimation | | Hiroyasu Tsukamoto
; Soon-Jo Chung
; Jean-Jacques E. Slotine
; | | Date: |
6 Nov 2020 | | Abstract: | We present neural stochastic contraction metrics, a new design framework for
provably-stable robust control and estimation for a class of stochastic
nonlinear systems. It exploits a spectrally-normalized deep neural network to
construct a contraction metric, sampled via simplified convex optimization in
the stochastic setting. Spectral normalization constrains the state-derivatives
of the metric to be Lipschitz continuous, and thereby ensures exponential
boundedness of the mean squared distance of system trajectories under
stochastic disturbances. This framework allows autonomous agents to approximate
optimal stable control and estimation policies in real-time, and outperforms
existing nonlinear control and estimation techniques including the
state-dependent Riccati equation, iterative LQR, EKF, and deterministic neural
contraction metric method, as illustrated in simulations. | | Source: | arXiv, 2011.03168 | | Services: | Forum | Review | PDF | Favorites | |
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