Actor-Critic Physics-informed Neural Lyapunov Control

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