Contraction theory: Hausdorff--Riemann Measures as Set-Based Lyapunov Functions
A. Matveev, A. Pogromsky

TL;DR
This paper extends contraction theory using measure-theoretic concepts, introducing Hausdorff-Riemann measures as set-based Lyapunov functions to analyze stability and contraction properties of nonlinear systems.
Contribution
It introduces a measure-theoretic extension of contraction theory with Hausdorff-Riemann measures, providing new tools for stability analysis and feedback design in nonlinear systems.
Findings
Derived necessary and sufficient conditions for d-contraction.
Introduced Hausdorff-Riemann measures as Lyapunov functions.
Applied criteria to stability of periodic solutions in various systems.
Abstract
We offer a measure-theoretic extension of the concept and theory of -contraction, including their generalization on fractional dimensions . The respective contraction property is defined through the exponential decay of the -dimensional volume of compact sets transported by a nonlinear flow. For autonomous systems on positively invariant compact sets, we derive comprehensive, i.e., necessary and sufficient, conditions for -contractivity in two complementary forms. The first is expressed in terms of the finite-time Lyapunov characteristic exponents and is akin in spirit to the first Lyapunov method. The second one is consonant with the second Lyapunov method and relies on existence of a Riemannian metric ensuring exponential decay of the metric-induced -dimensional Hausdorff measure. To acquire monotone measure-theoretic-based Lyapunov functions, we introduce a family of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsControl and Stability of Dynamical Systems · Stability and Controllability of Differential Equations · Adaptive Control of Nonlinear Systems
