Bilateral Solution Bounds and Successive Estimation of Boundedness and Stability Regions for Vector Delay Nonlinear Time-Varying Systems
Mark A. Pinsky

TL;DR
This paper introduces a novel method for estimating the boundedness and stability regions of vector nonlinear systems with variable delays, improving accuracy and reducing conservatism in stability analysis.
Contribution
It develops a successive approximation scheme and residual norm estimates to construct bilateral bounds and stability criteria for complex delay systems.
Findings
Bounds rapidly approach reference boundaries with iterations
Bilateral bounds converge as initial conditions remain within regions
Proposed method effectively estimates stability regions
Abstract
Stability and boundedness analysis for vector nonlinear systems with variable delays and coefficients remains challenging due to the conservatism of existing methods. Moreover, estimates of the transient behavior of solution norms remain insufficiently developed. This paper presents an approach to estimate the temporal evolution of solution norms and applies it to the analysis of boundedness and stability of vector nonlinear systems with variable delays and coefficients. The method is based on a novel scheme for successive approximations of the original solutions, complemented by the estimates of the corresponding residual norms. This leads to the construction of a scalar nonlinear delay equation whose solutions provide upper bounds for the evolution of residual norms. As a result, bilateral bounds on the original solution norms are obtained, yielding effective boundedness and stability…
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
TopicsStability and Control of Uncertain Systems · Numerical methods for differential equations · Model Reduction and Neural Networks
