Adaptive deep neural network controllers with Lyapunov-based weight updates are claimed to keep the tracking error of stochastic nonlinear systems uniformly bounded in probability, even with non-vanishing noise.
Stabilization of stochastic nonlinear systems driven by noise of unknown covariance,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 1years
2024 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems
Adaptive deep neural network controllers with Lyapunov-based weight updates are claimed to keep the tracking error of stochastic nonlinear systems uniformly bounded in probability, even with non-vanishing noise.