no code implementations • 15 Mar 2024 • Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou
In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis.
1 code implementation • 15 Mar 2024 • Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou
While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stability regions remain challenging due to the curse of dimensionality.
no code implementations • 15 Feb 2024 • Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu
We provide a theoretical analysis of both algorithms in terms of convergence of neural approximations towards the true optimal solutions in a general setting.
no code implementations • 20 Dec 2023 • Chuanzheng Wang, Yiming Meng, Jun Liu, Stephen Smith
Control barrier functions are widely used to synthesize safety-critical controls.
no code implementations • 14 Dec 2023 • Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou
We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions.
no code implementations • 8 Mar 2023 • Yiming Meng, Jun Liu
The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems.
no code implementations • 22 May 2022 • Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.
no code implementations • 6 Apr 2021 • Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).
no code implementations • 9 Sep 2020 • Yiming Meng, Yinan Li, Maxwell Fitzsimmons, Jun Liu
While the converse Lyapunov-barrier theorems are not constructive, as with classical converse Lyapunov theorems, we believe that the unified necessary and sufficient conditions with a single Lyapunov-barrier function are of theoretical interest and can hopefully shed some light on computational approaches.