This paper provides sufficient conditions for global asymptotic stability and global exponential stability, which can be applied to nonlinear, large-scale, uncertain discrete-time systems. The conditions are derived by means of vector Lyapunov functions. The obtained results are applied to traffic networks for the derivation of sufficient conditions of global exponential stability of the uncong...

In this paper, we investigate the problem of global and robust stability of a class of interval Hopfield neural networks that have time-varying delays. Some criteria for the global and robust stability of such networks are derived, by means of constructing suitable Lyapunov functionals for the networks. As a by-product, for the conventional Hopfield neural networks with time-varying delays, we ...

Global asymptotic stability conditions for discrete nonlinear scalar stochastic systems with state delay are obtained based on the convergence theorem for semimartingale inequalities, without assuming the Lipschitz conditions for nonlinear drift functions. The Lyapunov-Krasovskii and degenerate functionals techniques are used. The derived stability conditions are directly expressed in terms of ...

AMract-We present a methodology based on the energy shaping framework to derive strict Lyapunov functions for a class of global regulators for robot manipulators. The class of controllers is described by control laws composed by the gradient of an artificial potential energy plus a linear velocity feedback. We provide explicit sufficient conditions on the artificial potential energy that allow ...

This paper investigates the uniqueness and globally uniformly asymptotic stability for a class of memristor-based recurrent neural networks with time-varying delays. By employing a homeomorphism and suitable Lyapunov functional and differential condition, a sufficient conclusion for the uniqueness and globally uniformly asymptotic stability of a class of memristor-based recurrent neural network...