Shen-Ping Xiao

Passivity analysis for neural networks with a time-varying delay

This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties by employing an improved free-weighting matrix approach. Some useful terms have been retained, which were used to be ignored in the derivative of Lyapunov-Krasovskii functional. Furthermore, the relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, for two types of time-varying delays, less conservative delay-dependent passivity conditions are obtained in terms of linear matrix inequalities (LMIs), respectively. Finally, a numerical example is given to demonstrate the effectiveness of the proposed techniques.

Improved results on passivity analysis of neural networks with time-varying discrete and distributed delays

In this paper, the passivity analysis problem is investigated for uncertain neural networks with time-varying discrete and distributed delays. Based on direct delay decomposition idea and free-weighting matrix approach, several new delay-dependent passive criterions are derived in terms of linear matrix inequalities (LMIs), which can be easily checked by the Matlab LMI toolbox. Numerical examples show that the obtained results improve some existing ones.

Stability analysis of generalized neural networks with time-varying delays via a new integral inequality

This paper focuses on the delay-dependent stability of a class of generalized neural networks (NNs) with time-varying delays. A free-matrix-based inequality is presented by introducing a set of slack variables, which encompasses the Wirtinger-based inequality as a special case. Then, by constructing a suitable Lyapunov-Krasovskii functional and utilizing the new inequality to bound the derivative of the Lyapunov-Krasovskii functional, some sufficient conditions are derived to assure the stability of the considered neural networks. Three numerical examples are provided to demonstrate the effectiveness and the significant improvement of the proposed method.

Dissipativity analysis of neural networks with time-varying delays

This paper focuses on the problem of delay-dependent dissipativity analysis for a class of neural networks with time-varying delays. A free-matrix-based inequality method is developed by introducing a set of slack variables, which can be optimized via existing convex optimization algorithms. Then, by employing Lyapunov functional approach, sufficient conditions are derived to guarantee that the considered neural networks are strictly ( Q , S , R ) -γ-dissipative. The conditions are presented in terms of linear matrix inequalities and can be readily checked and solved. Numerical examples are finally provided to demonstrate the effectiveness and advantages of the proposed new design techniques.

New Results on

This study deals with the problem of H∞ tracking control for a sampled-data networked control system based on a Takagi-Sugeno fuzzy model. An error model is established by combining the input delay and parallel distributed compensation techniques so as to transform the sampling period of a sampler, a signal transmission delay, and data packet dropouts to the refreshing interval of a zero-order hold. The method introduces a new augmented Lyapunov-Krasovskii functional to derive a sufficient condition to ensure a prescribed H∞ tracking performance with less conservativeness than others. A fuzzy controller can easily be designed using the condition. A numerical example demonstrates the validity of the method.

H_\infty

In this paper, we focus on deriving a less conservative stability criterion for generalized neural networked (GNN) with interval time-varying delays. Firstly, a new augmented Lyapunov-Krasovskii functional (ALKF) with triple-integral terms is chosen to reduce the conservatism of stability criteria. In addition, by fully utilizing the integral inequality presented recently and combining the reciprocally convex approach, a improved delay-dependent stability condition is established in terms of LMI. Finally, two numerical examples are provided to verify the advantages of the presented criteria.

Tracking Control Based on the T–S Fuzzy Model for Sampled-Data Networked Control System

This study is concerned with the problem of delay-dependent passivity analysis for neural networks with discrete and distributed delays. By employing a new integral inequality and the convex combination approach to estimate the derivative of Lyapunov-Krasovskii functional, sufficient conditions are established to ensure that the considered neural network is passive. A given numerical example demonstrates the effectiveness of the proposed method.

Improved delay-dependent stability criterion of generalized neural networked with interval time-varying delays

This paper is focused on the H∞ control problem for linear systems with interval time-varying delays. By employing a reciprocally convex combination approach and a delay decomposition approach, some new delay-dependent bounded real lemmas (BRLs) are derived such that the closed-loop system is asymptotically stable with a prescribed H∞ level. The BRLs are then used to solve the H∞ controller design by incorporating with the cone complementary approach. Three numerical examples are finally given to show the validity of the proposed method.

Passivity analysis of neural networks with discrete and distributed delays

This paper studies the stability analysis for a class of networked control systems with time-delay. Based on the general model of networked control systems, by choosing proper Lyapunov-Krasovskii functional and applying the Jensen inequality and the Wirtingers inequality comprehensively, we obtain less conservative LMI-based delay-dependent stability condition. Finally, we verify the sufficient condition through MATLAB stimulation and the numerical example shows that the result obtained in this study is better than some published results.

New results on H∞ control of linear systems with interval time-varying delays

This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties. By further utilizing the information of activation function and employing a reciprocally convex approach to consider the relationship between the time-varying delay and its time-varying interval, some improved delay-dependent passivity conditions are obtained, which are formulated in terms of linear matrix inequalities (LMIs) and can be readily solved by existing convex optimization algorithms. Finally, a numerical example is provided to verify the effectiveness of the proposed techniques.