Exponential Lagrange Stability for Markovian Jump Uncertain Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Impulsive Control

Abstract

The problem of exponential Lagrange stability analysis of Markovian jump neural networks with leakage delay and mixed timevarying delays is studied in this paper. By utilizing the Lyapunov functional method, employing free-weighting matrix approach and inequality techniques in matrix form, we establish several novel stability criteria such that, for all admissible parameter uncertainties, the suggested neural network is exponentially stable in Lagrange sense. The derived criteria are expressed in terms of linear matrix inequalities (LMIs). A numerical example is provided to manifest the validity of the proposed results.

A new fuzzy time-delay control for cable-driven robot

“…The max absolute errors (MAE) and mean square errors (MSE) and quantization errors of joints are calculated in Table 4. [27][28][29][30] According to the results listed in Table 4, TDC has greater advantage than the conventional PD controller. To meet trajectory tracking with high precision, higher gains are needed for the conventional PD controller with slower convergence of errors.…”

“…The max absolute errors (MAE) and mean square errors (MSE) and quantization errors of joints are calculated in Table 4. 27 –30…”