38 citations · 95 across the 14 of their papers we have counts for
11 papers
Koopman-Based Neural Lyapunov Functions for General Attractors
Shankar A. Deka, Alonso M. Valle, Claire J. Tomlin
Koopman spectral theory has grown in the past decade as a powerful tool for dynamical systems analysis and control. In this paper, we show how recent data-driven techniques for est…
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil +3
This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient para…
Robust Sequential Path Planning Under Disturbances and Adversarial Intruder
Mo Chen, Somil Bansal, Jaime F. Fisac +1
Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Althou…
Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions
Frank Jiang, Glen Chou, Mo Chen +1
To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neura…
Exact and Efficient Hamilton-Jacobi-based Guaranteed Safety Analysis via System Decomposition
Mo Chen, Sylvia Herbert, Claire J. Tomlin
Hamilton-Jacobi (HJ) reachability is a method that provides rigorous analyses of the safety properties of dynamical systems. This method has been successfully applied to many low-d…
Compressed Sensing for Network Reconstruction
David Hayden, Young Hwan Chang, Jorge Goncalves +1
The problem of identifying sparse solutions for the link structure and dynamics of an unknown linear, time-invariant network is posed as finding sparse solutions x to Ax=b. If the…