Invention Grant
- Patent Title: Dynamics model for globally stable modeling of system dynamics
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Application No.: US16933245Application Date: 2020-07-20
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Publication No.: US11886782B2Publication Date: 2024-01-30
- Inventor: Gaurav Manek , Jeremy Zieg Kolter , Julia Vinogradska
- Applicant: Robert Bosch GmbH , Carnegie Mellon University
- Applicant Address: DE PA Stuttgart
- Assignee: ROBERT BOSCH GMBH
- Current Assignee: ROBERT BOSCH GMBH
- Current Assignee Address: DE Stuttgart
- Agency: NORTON ROSE FULBRIGHT US LLP
- Priority: EP 190105 2019.08.05
- Main IPC: G06F30/27
- IPC: G06F30/27 ; G06N20/00 ; G05B13/02 ; G05B13/04 ; G06N3/08 ; G06N5/046

Abstract:
A system and computer-implemented method are provided for training a dynamics model to learn the dynamics of a physical system. The dynamics model may be learned to be able to infer a future state of the physical system and/or its environment based on a current state of the physical system and/or its environment. The learned dynamics model is inherently globally stable. Instead of learning a dynamics model and attempting to separately verify its stability, the learnable dynamics model comprises a learnable Lyapunov function which is jointly learned together with the nominal dynamics of the physical system. The learned dynamics model is highly suitable for real-life applications in which a physical system may assume a state which was unseen during training as the learned dynamics model is inherently globally stable.
Public/Granted literature
- US20210042457A1 DYNAMICS MODEL FOR GLOBALLY STABLE MODELING OF SYSTEM DYNAMICS Public/Granted day:2021-02-11
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