A criterion of existence and uniqueness of the Lyapunov matrix for a class of time delay systems

The Lyapunov matrix for systems of linear time-delay equations is a matrix-valued function which is a solution of a special dynamic system with some additional boundary conditions. This matrix allows to construct the complete type Lyapunov–Krasovskii functionals with a prescribed derivative, which are used successfully in analysis of behavior of time-delay systems. It was shown in works of Kharitonov and Chashnikov that the Lyapunov condition, that is the absence of opposite eigenvalues of the system, guarantees the existence and uniqueness of the Lyapunov matrix for systems of retarded type with multiple delays and for systems of neutral type with a single delay. In this contribution, we consider a linear time-invariant system of neutral type with two delays. It is shown that under a certain constraint the Lyapunov condition, that is the absence of opposite eigenvalues of the system, is also a criterion of the existence and uniqueness of the Lyapunov matrix. Refs 14

Performance Improvement in Noisy Linear Consensus Networks with Time-Delay

We analyze performance of a class of time-delay first-order consensus networks from a graph topological perspective and present methods to improve it. The performance is measured by network's square of H-2 norm and it is shown that it is a convex function of Laplacian eigenvalues and the coupling weights of the underlying graph of the network. First, we propose a tight convex, but simple, approximation of the performance measure in order to achieve lower complexity in our design problems by eliminating the need for eigen-decomposition. The effect of time-delay reincarnates itself in the form of non-monotonicity, which results in nonintuitive behaviors of the performance as a function of graph topology. Next, we present three methods to improve the performance by growing, re-weighting, or sparsifying the underlying graph of the network. It is shown that our suggested algorithms provide near-optimal solutions with lower complexity with respect to existing methods in literature.Comment: 16 pages, 11 figure

Computing delay Lyapunov matrices and H2 norms for large-scale problems

A delay Lyapunov matrix corresponding to an exponentially stable system of linear time-invariant delay differential equations can be characterized as the solution of a boundary value problem involving a matrix valued delay differential equation. This boundary value problem can be seen as a natural generalization of the classical Lyapunov matrix equation. We present a general approach for computing delay Lyapunov matrices and H2 norms for systems with multiple discrete delays, whose applicability extends towards problems where the matrices are large and sparse, and the associated positive semidefinite matrix (the ``right-hand side' for the standard Lyapunov equation), has a low rank. In contract to existing methods that are based on solving the boundary value problem directly, our method is grounded in solving standard Lyapunov equations of increased dimensions. It combines several ingredients: i) a spectral discretization of the system of delay equations, ii) a targeted similarity transformation which induces a desired structure and sparsity pattern and, at the same time, favors accurate low rank solutions of the corresponding Lyapunov equation, and iii) a Krylov method for large-scale matrix Lyapunov equations. The structure of the problem is exploited in such a way that the final algorithm does not involve a preliminary discretization step, and provides a fully dynamic construction of approximations of increasing rank. Interpretations in terms of a projection method directly applied to a standard linear infinite-dimensional system equivalent to the original time-delay system are also given. Throughout the paper two didactic examples are used to illustrate the properties of the problem, the challenges and methodological choices, while numerical experiments are presented at the end to illustrate the effectiveness of the algorithm