In this paper, the global uniform asymptotic stability is studied for a class of delayed neutral-type neural networks with reactiondiffusion terms. By constructing appropriate Lyapunov-Krasovskii functional and using the linear matrix inequality (LMI) approach, several sufficient conditions are obtained for ensuring the system to be globally uniformly asymptotically stable. A numerical example ...

This paper considers the global robust exponential stability of time-varying delayed neural networks with discontinuous activation functions and norm-bounded uncertainties. Based on the Lyapunov– Krasovskii stability theory, we originally analyze the global robust exponential stability of discontinuous neural networks with time-varying delays in view of the linear matrix inequalities given to v...

For cellular neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov–Krasovskii functional and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived. Two examples are given t...

The robust exponential stability is investigated for a class of uncertain neutral-type neural networks with discrete and distributed time-varying delays. By introducing a new vector Lyapunov-Krasovskii functional, using Jensen integral inequality, free-weighting matrix method and linear matrix inequality techniques, delay-dependent sufficient conditions are obtained for exponential stability of...

A problem of assessing stability of retarded dynamical networks is solved in this paper. Subsystems are assumed to be integral input-to-state stable (iISS). Time-delays are allowed to reside in both subsystems and interconnection channels, and may be both discrete and distributed. No assumption is made on the interconnection topology. This paper develops a small-gain methodology for constructin...

This paper presents an adaptive tracking control method for a class of nonlinearly parameterized MIMO dynamic systems with time-varying delay and unknown nonlinear dead-zone inputs. A new high dimensional integral Lyapunov-Krasovskii functional is introduced for the adaptive controller to guarantee global stability of the considered systems and also ensure convergence of the tracking errors to ...

This paper analyzes the stability of Linear Parameter Varying (LPV) time-delayed systems. Several delay-independent stability conditions are presented, which are derived using appropriately selected Lyapunov-Krasovskii functionals. Depending on the system parameter dependence, these functionals can be selected to obtain increasingly non-conservative results. Using relaxation methods and griddin...

This paper discusses about the stabilization of unknown nonlinear discrete-time fixed state delay systems. The unknown system nonlinearity is approximated by Chebyshev neural network (CNN), and weight update law is presented for approximating the system nonlinearity. Using appropriate Lyapunov-Krasovskii functional the stability of the nonlinear system is ensured by the solution of linear matri...

This paper addresses the stabilization for a class of high-order time-delay nonlinear systems. Under some essential restriction on the system growth, by the method of adding a power integrator, a continuous state-feedback controller is successfully designed, and the global asymptotic stability of the resulting closed-loop system is proven with the help of an appropriate Lyapunov-Krasovskii func...

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion (FSE) are combined into a new function approximator to model each suitable disturbed function in systems. The require...