no code implementations • 5 May 2024 • Ruikun Zhou, Wail Gueaieb, Davide Spinello
We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, along with the anomaly locations and their relative sizes.
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 • 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 • 4 Oct 2023 • Amartya Mukherjee, Ruikun Zhou, Haocheng Chang, Jun Liu
This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems.
1 code implementation • 4 Jun 2022 • Ruikun Zhou, Thanin Quartz, Hans De Sterck, Jun Liu
This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system.