S. Marshal Anthoni

Robust mixed H ∞ and passive filtering for networked Markov jump systems with impulses

This paper deals with the problem of mixed H ∞ and passive filter design for Markovian jump impulsive networked control systems with norm bounded uncertainties and random packet dropouts. The system under consideration is modeled by means of an impulsive subsystem, and the network-induced imperfections like packet dropout and delay are described by a Bernoulli distributed white sequence. The delay in the model is assumed to be time-varying. The mode-dependent conditions are established to guarantee the filtering error system to be robustly stochastically stable and achieve a prescribed mixed H ∞ and passivity performance index. The sufficient conditions for the existence of admissible filters are expressed by using the linear matrix inequality (LMI) approach and convex optimization problem. The corresponding filter parameters can be obtained by solving the set of LMIs, which can be easily facilitated by using some standard numerical packages. Finally, a numerical example is given to illustrate the effectiveness and potential of the proposed filter design. HighlightsThis paper considers mixed H ∞ and passive filter design for Markovian jump impulsive networked control systems.System is subjected to norm bounded uncertainties and random packet dropouts.The mode-dependent conditions are established to guarantee the filtering error system to be robustly stochastically stable and achieve a prescribed performance index.A numerical example has been finally provided to illustrate the performance and effectiveness of the developed approach.

Robust passivity analysis of fuzzy Cohen–Grossberg BAM neural networks with time-varying delays

Abstract This paper is concerned with the problem of passivity analysis for a class of Cohen–Grossberg fuzzy bidirectional associative memory (BAM) neural networks with time varying delay. By employing the delay fractioning technique and linear matrix inequality optimization approach, delay dependent passivity criteria are established that guarantees the passivity of fuzzy Cohen–Grossberg BAM neural networks with uncertainties. The passivity condition is expressed in terms of LMIs, which can be easily solved by various convex optimization algorithms. Finally, a numerical example is given to illustrate the effectiveness of the proposed result.

New stability and stabilization criteria for fuzzy neural networks with various activation functions

In this paper, the stability analysis and control design of Takagi–Sugeno (TS) fuzzy neural networks with various activation functions and continuously distributed time delays are addressed. By implementing the delay-fractioning technique together with the linear matrix inequality (LMI) approach , a new set of sufficient conditions is derived in terms of linear matrix inequalities, which ensure the stability of the considered fuzzy neural networks. Further, based on the above-mentioned techniques, a control law with an appropriate gain control matrix is derived to achieve stabilization of the fuzzy neural networks. In addition, the results are extended to the study of the stability and stabilization results for TS fuzzy uncertain neural networks with parameter uncertainties. The stabilization criteria are obtained in terms LMIs and hence the gain control matrix can be easily determined by the MATLAB LMI control toolbox. Two numerical examples with simulation results are given to illustrate the effectiveness of the obtained result.

Robust stability and control for uncertain neutral time delay systems

In this article, the problem of robust stability and stabilisation for a class of uncertain neutral systems with discrete and distributed time delays is considered. By utilising a new Lyapunov functional based on the idea of delay partitioning approach, we employ the linear matrix inequality technique to derive delay-dependent criteria which ensures the robust stability of uncertain neutral systems. The obtained stability conditions are formulated in terms of linear matrix inequalities that can easily be solved by using standard software packages. Further, the result is extended to study the robust stabilisation for uncertain neutral systems with parameter uncertainties. A state feedback controller is proposed to guarantee the robust asymptotic stabilisation for uncertain systems and the controller is constructed in terms of the solution to a set of matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and conservatism of the obtained results. It is shown that the results developed in this article can tolerate larger allowable delay than some existing works in the literature. Further, it is proved that the proposed criterion is also computationally less conservative when compared to some existing results.

EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS

This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.

Delay fractioning approach to robust exponential stability of fuzzy Cohen–Grossberg neural networks ☆

Abstract In this paper, the problem of robust exponential stability analysis for a class of Takagi–Sugeno (TS) fuzzy Cohen–Grossberg neural networks with uncertainties and time-varying delays is investigated. A generalized activation function is used, and the assumptions such as boundedness, monotony and differentiability of the activation functions are removed. By using a Lyapunov–Krasovskii functional and employing the delay fractioning approach, a set of sufficient conditions are established for achieving the required result. The obtained conditions are proposed in terms of linear matrix inequalities (LMIs), so its feasibility can be checked easily via standard numerical toolboxs. The main advantage of the proposed criteria lies in its reduced conservatism which is mainly based on the time delay fractioning technique. In addition to that, a numerical example with simulation results is given to show the effectiveness of the obtained LMI conditions.

Asymptotic stability of delayed stochastic genetic regulatory networks with impulses

In this paper, the asymptotic stability analysis problem is considered for a class of delayed stochastic genetic regulatory networks with impulses. Based on the Lyapunov stability technique and stochastic analysis theory, stability criteria are proposed in terms of linear matrix inequalities (LMI). It is shown that the addressed stochastic genetic regulatory networks are globally asymptotically stable if four LMIs are feasible, where the feasibility of LMIs can be readily checked by Matlab LMI toolbox. Finally, a numerical example is given to demonstrate the usefulness of the proposed result.

Design of a passification controller for uncertain fuzzy Hopfield neural networks with time-varying delays

This paper addresses the problem of controller design for passivity of uncertain fuzzy Hopfield neural networks with time-varying delays. The main purpose of this paper is to design a state feedback fuzzy controller such that the resulting closed-loop system is passive. A new set of sufficient conditions are derived for achieving the required result by employing the Lyapunov functional method and matrix analysis technique. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked using standard numerical software. Two numerical examples with simulation results are given to illustrate the effectiveness and conservatism of the obtained results.

Robust state estimation for discrete-time BAM neural networks with time-varying delay

This paper is concerned with the robust delay-dependent state estimation problem for a class of discrete-time Bidirectional Associative Memory (BAM) neural networks with time-varying delays. By using the Lyapunov-Krasovskii functional together with linear matrix inequality (LMI) approach, a new set of sufficient conditions are derived for the existence of state estimator such that the error state system is asymptotically stable. More precisely, an LMI-based state estimator and delay-dependent stability criterion for delayed BAM neural networks are developed. The conditions are established in terms of LMIs which can be solved by the MATLAB LMI toolbox. It should be mentioned that all the sufficient conditions are dependent on the upper and lower bounds of the delays. Also, the desired estimator unknown gain matrix is determined in terms of the solution to these LMIs. Finally, numerical examples with simulation results are given to illustrate the effectiveness and applicability of the obtained results.

New robust exponential stability results for discrete-time switched fuzzy neural networks with time delays

This paper provides a novel result on robust exponential stability for a class of uncertain discrete-time switched fuzzy neural networks (DSFNNs) with time-varying delays and parameter uncertainties. By implementing an average dwell time approach with a new Lyapunov-Krasovskii functional, we obtain some delay-dependent sufficient conditions guaranteeing the robust exponential stability of the considered switched fuzzy neural networks. In other words, a class of switching signals specified by the average dwell time is identified to guarantee the exponential stability of the considered DSFNNs. The obtained conditions are formulated in terms of Linear Matrix Inequalities (LMIs) which can be easily verified via the LMI toolbox. Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained results.