| | |
| | |
Stat |
Members: 3645 Articles: 2'500'096 Articles rated: 2609
19 April 2024 |
|
| | | |
|
Article forum
| |
|
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 |
|
|
No message found in this article forum.
You have a question or message about this article?
Ask the community and write a message in the forum.
If you want to rate this article, please use the review section..
To add a message in the forum, you need to login or register first. (free): registration page
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |