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Positive Forms and Stability of Linear Time-Delay Systems

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TLDR
An explicit parametrization of a finite-dimensional subset of the cone of Lyapunov functions is given, enforced using sum-of-squares polynomial matrices, which allows the computation to be formulated as a semidefinite program.
Abstract
We consider the problem of constructing Lyapunov functions for linear differential equations with delays. For such systems it is known that exponential stability implies the existence of a positive Lyapunov function which is quadratic on the space of continuous functions. We give an explicit parameterization of a sequence of finite-dimensional subsets of the cone of positive Lyapunov functions using positive semidefinite matrices. This allows stability analysis of linear time-delay systems to be formulated as a semidefinite program.

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Citations
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Journal ArticleDOI

Brief paper: A novel stability analysis of linear systems under asynchronous samplings

TL;DR: This article proposes a novel approach to assess the stability of continuous linear systems with sampled-data inputs based on the discrete-time Lyapunov theorem, which provides easy tractable stability conditions for the continuous-time model.
Journal ArticleDOI

Hierarchy of LMI conditions for the stability analysis of time-delay systems☆

TL;DR: The present paper aims at providing a generic set of integral inequalities which are asymptotically non conservative and then to design functionals driven by these inequalities, which form a hierarchy of LMI which is competitive with the most efficient existing methods.
Journal ArticleDOI

Technical communique: A delay-partitioning approach to the stability analysis of discrete-time systems

TL;DR: By utilizing the delay partitioning idea, new stability criteria are proposed in terms of linear matrix inequalities (LMIs) based on a novel Lyapunov functional, which establishes that the conservatism of the conditions is a non-increasing function of the number of partitions.
Journal ArticleDOI

Dwell-time stability and stabilization conditions for linear positive impulsive and switched systems

TL;DR: The proposed stability conditions characterize the pointwise decrease of a linear copositive Lyapunov function and are formulated in terms of finite-dimensional or semi-infinite linear programs for which three asymptotically exact computational methods are proposed and compared with each other on numerical examples.
Journal ArticleDOI

Survey on time-delay approach to networked control

TL;DR: The survey highlights time-delay approach developed to modelling, analysis and synthesis of NCSs, under communication constraints, with a particular focus on Round-Robin, Try-once-discard and stochastic protocols.
References
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Journal ArticleDOI

Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones

TL;DR: This paper describes how to work with SeDuMi, an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints by exploiting sparsity.
Book

Stability of Time-Delay Systems

TL;DR: Preface, Notations 1.Introduction to Time-Delay Systems I.Robust Stability Analysis II.Input-output stability A.LMI and Quadratic Integral Inequalities Bibliography Index
Journal ArticleDOI

Semidefinite programming

TL;DR: A survey of the theory and applications of semidefinite programs and an introduction to primaldual interior-point methods for their solution are given.
Journal ArticleDOI

Global Optimization with Polynomials and the Problem of Moments

TL;DR: It is shown that the problem of finding the unconstrained global minimum of a real-valued polynomial p(x): R n to R, in a compact set K defined byPolynomial inequalities reduces to solving an (often finite) sequence of convex linear matrix inequality (LMI) problems.
DissertationDOI

Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization

TL;DR: In this paper, the authors introduce a specific class of linear matrix inequalities (LMI) whose optimal solution can be characterized exactly, i.e., the optimal value equals the spectral radius of the operator.