March 14, 2024, 4:41 a.m. | Jiarui Wang, Mahyar Fazlyab

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.08448v1 Announce Type: new
Abstract: Designing control policies for stabilization tasks with provable guarantees is a long-standing problem in nonlinear control. A crucial performance metric is the size of the resulting region of attraction, which essentially serves as a robustness "margin" of the closed-loop system against uncertainties. In this paper, we propose a new method to train a stabilizing neural network controller along with its corresponding Lyapunov certificate, aiming to maximize the resulting region of attraction while respecting the actuation …

abstract actor actor-critic arxiv control cs.lg cs.ro cs.sy designing eess.sy loop paper performance physics physics-informed robustness tasks type

Senior Data Engineer

@ Displate | Warsaw

Principal Software Engineer

@ Microsoft | Prague, Prague, Czech Republic

Sr. Global Reg. Affairs Manager

@ BASF | Research Triangle Park, NC, US, 27709-3528

Senior Robot Software Developer

@ OTTO Motors by Rockwell Automation | Kitchener, Ontario, Canada

Coop - Technical Service Hub Intern

@ Teradyne | Santiago de Queretaro, MX

Coop - Technical - Service Inside Sales Intern

@ Teradyne | Santiago de Queretaro, MX