Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 82 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 16 tok/s Pro
GPT-4o 117 tok/s Pro
Kimi K2 200 tok/s Pro
GPT OSS 120B 469 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

A Linear Differential Inclusion for Contraction Analysis to Known Trajectories (2411.11587v1)

Published 18 Nov 2024 in eess.SY, cs.SY, and math.OC

Abstract: Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of the system. In this note, we study contraction of a system to a particular known trajectory, deriving a new LDI characterizing the error between arbitrary trajectories and this known trajectory. As with classical contraction analysis, this new inclusion is constructed via first partial derivatives of the system's vector field, and contraction rates are obtained with familiar tools: uniform bounding of the logarithmic norm and LMI-based Lyapunov conditions. Our LDI is guaranteed to outperform a usual contraction analysis in two special circumstances: i) when the bound on the logarithmic norm arises from an interval overapproximation of the Jacobian matrix, and ii) when the norm considered is the $\ell_1$ norm. Finally, we demonstrate how the proposed approach strictly improves an existing framework for ellipsoidal reachable set computation.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.