Discontinuous Lyapunov functional approach to synchronization of time-delay neural networks using sampled-data

This paper investigates the synchronization problem of neural networks with time-varying delay under sampled-data control in the presence of a constant input delay. Based on the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced, which makes full use of the sawtooth structure characteristic of sampling input delay. A simple and less conservative synchronization criterion is given to ensure the master systems synchronize with the slave systems by using the linear matrix inequality (LMI) approach. The design method of the desired sampled-data controller is also proposed. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.     

Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach

This paper is concerned with the sampled-data state estimation problem for neural networks with both Markovian jumping parameters and leakage time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. In order to make full use of the sawtooth structure characteristic of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. A less conservative delay dependent stability criterion is derived via constructing a new triple-integral Lyapunov–Krasovskii functional and the famous Jenson integral inequality. Based on the Lyapunov–Krasovskii functional approach, a state estimator of the considered neural networks has been achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, two numerical examples are provided to show the effectiveness of the proposed methods.     

An enhanced input-delay approach to sampled-data stabilization of T–S fuzzy systems via mixed convex combination

The problem of sampled-data control is investigated for Takagi–Sugeno (T–S) fuzzy systems with aperiodic sampling intervals based on an enhanced input-delay approach. Delay-dependent stability and stabilizability conditions for the closed-loop continuous nonuniformly sampled-data fuzzy systems are derived by constructing a novel discontinuous Lyapunov–Krasovskii (L–K) functional, which makes good use of not only the upper bound on the variable sampling interval, but also its sawtooth structure information about varying input delay often ignored in previous results. A bounding technique combined by reciprocally convex technics and linear convex combination is presented for acquiring the time derivative of the functional, wherein Jensen’s inequality and Wirtinger’s inequality are integratively employed. And a feasible solution of the obtained criterion formulated as parameterized linear matrix inequalities is ultimately conceived. A numerical example is given to show the effectiveness of the proposed method.     

Sampled-data state estimation for delayed neural networks with Markovian jumping parameters

This paper is concerned with the sampled-data state estimation problem for a class of delayed neural networks with Markovian jumping parameters. Unlike the classical state estimation problem, in our state estimation scheme, the sampled measurements are adopted to estimate the concerned neuron states. The neural network under consideration is assumed to have multiple modes that switch from one to another according to a given Markovian chain. By utilizing the input delay approach, the sampling period is converted into a time-varying yet bounded delay. Then a sufficient condition is given under which the resulting error dynamics of the neural networks is exponentially stable in the mean square. Based on that, a set of sampled-data estimators is designed in terms of the solution to a set of linear matrix inequalities (LMIs) which can be solved by using the available software. Finally, a numerical example is used to show the effectiveness of the estimation approach proposed in this paper.     

Exponential state estimation for delayed recurrent neural networks with sampled-data

In this paper, the sampled-data state estimation problem is investigated for a class of recurrent neural networks with time-varying delay. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. By converting the sampling period into a bounded time-varying delay, the error dynamics of the considered neural network is derived in terms of a dynamic system with two different time-delays. Subsequently, by choosing an appropriate Lyapunov functional and using the Jensen??s inequality, a sufficient condition depending on the sampling period is obtained under which the resulting error system is exponentially stable. Then a sampled-data estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs) which can be solved by using available software. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed sampled-data estimation approach.     

State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach

In this paper, the sampled-data state estimation problem is investigated for neural networks with time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled data estimator is constructed. Based on the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced, which makes full use of the sawtooth structure characteristic of sampling input delay. New delay-dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulations are given to demonstrate the usefulness and effectiveness of the presented results.     

Global stabilization of high-order nonlinear systems under multi-rate sampled-data control

This paper studies the sampled-data control problem for a class of high-order nonlinear systems. Based on exact discrete-time equivalent model of the sampled-data system, a multi-rate sampled-data controller with the form of a power series expansion is designed to achieve the global asymptotic stability of the closed-loop system under some assumptions. Approximate solutions of the proposed controller are proved to be effective by a theoretical analysis. The results show that, compared with the emulated control scheme, the approximate controllers allow considering larger sampling periods and enlarge the domain of attraction for a given sampling period. Finally, a simulation example is given to show the effectiveness of the proposed control scheme.     

Further results on memory control of nonlinear discrete-time networked control systems with random input delay

This paper is concerned with the problem of memory state feedback stabilization for a class of nonlinear discrete-time networked control systems with partly known probability distribution of input delay. Different from the common assumptions on the delay in the existing literatures, it is assumed that the probabilities of the delay taking values in a finite set are partly known or fully known in advance. In terms of the information about the occurrence probabilities of the delay taking values in the two finite sets, a new stochastic delay model is proposed, where the probability information of the delay is included in the parameter matrices of the transformed system. Based on the new model, a delay-dependent stabilization criterion is derived using a combination of the Lyapunov functional method, convexity of matrix inequalities, and linear matrix inequality technique. Finally, three illustrative examples are provided to show the effectiveness and applicability of the developed theoretical results.     

Dissipativity analysis of stochastic neural networks with time delays

This paper is concerned with the dissipativity problem of stochastic neural networks with time delay. A new stochastic integral inequality is first proposed. By utilizing the delay partitioning technique combined with the stochastic integral inequalities, some sufficient conditions ensuring mean-square exponential stability and dissipativity are derived. Some special cases are also considered. All the given results in this paper are not only dependent upon the time delay, but also upon the number of delay partitions. Finally, some numerical examples are provided to illustrate the effectiveness and improvement of the proposed criteria.     

Passivity analysis for discrete-time switched neural networks with various activation functions and mixed time delays

This paper is concerned with the passivity analysis for a class of discrete-time switched neural networks with various activation functions and mixed time delays. The mixed time delays under consideration include time-varying discrete delay and bounded distributed delay. By using the average dwell time approach and the discontinuous piecewise Lyapunov function technique, a novel delay-dependent sufficient condition for exponential stability of the switched neural networks with passivity is derived in terms of a set of linear matrix inequalities (LMIs). The obtained condition is not only dependent on the discrete delay bound, but also dependent on the distributed delay bound. A numerical example is given to demonstrate the effectiveness of the proposed result.     

Absolute stabilization related to circle criterion: An LMI-based approach

A method is proposed for synthesizing output feedback controllers for nonlinear Lur' e systems . The problem of designing an output dynamic controller for uncertain-free systems and systems subject to multiplicative norm-bounded perturbations in the linear part were proposed respectively. The procedure is based on the use of the absolute stability, through the circle criterion, and a linear matrix inequalities (LAI) formulation. The controller existence conditions are given in terms of existence of suitable solutions to a set of parameter-dependent LMIs.     

Sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays

In this letter, a distributed protocol for sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays is proposed, and a brief procedure of convergence analysis for such algorithm over undirected connected graphs is provided. Furthermore, a simple yet generic criterion is also presented to guarantee synchronized oscillatory motions in coupled harmonic oscillators. Subsequently, the simulation results are worked out to demonstrate the efficiency and feasibility of the theoretical results.     

Global asymptotical stability and generalized synchronization of phase synchronous dynamical networks

This paper examines the global asymptotical stability of the phase synchronous dynamical networks composed by a class of nonlinear pendulum-like systems with multiple equilibria. Sufficient conditions for the determination of global asymptotical stability are given in terms of linear matrix inequalities (LMIs). Furthermore, a concept of generalized synchronization is introduced, and the criterion of which is proposed in a simple form. Those results are of particular convenience for networks that possess large numbers of nodes, and they can be used to discuss controller design problems as well. Numerical simulations and analytical results are in excellent agreement with each other.     

State estimation for Markovian jumping recurrent neural networks with interval time-varying delays

The paper is concerned with the state estimation problem for a class of neural networks with Markovian jumping parameters. The neural networks have a finite number of modes and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time-delays, the dynamics of the estimation error are globally stable in the mean square. A new type of Markovian jumping matrix P _i is introduced in this paper. The discrete delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI conditions.     

静力预加载结构冲击屈曲的突变模型

基于突变理论给出静力预加载弹性结构冲击屈曲的准则,并将其用于处理Budian-sky-Hutchinson简单力学模型的冲击屈曲分析和受扭圆柱壳的冲击扭转屈曲分析,给出了临界载荷的求解公式。     

On Reliable Stabilization of Linear Periodic Systems

An algorithm is proposed to synthesize a reliable controller with a given stability margin for linear and periodic systems optimized with respect to a quadratic performance criterion. A reliable controller synthesized by the algorithm guarantees the stability margin and is close to the linear-quadratic requlator. The importance of ensuring the stability margin is demonstrated. The proposed algorithm is based on methods of linear matrix inequalities and can be implemented using standard MATLAB routines. As an example, a reliable controller that stabilizes the program motion of a hopping machine is synthesized __________ Translated from Prikladnaya Mekhanika, Vol. 41, No. 10, pp. 114–126, October 2005.     

Synchronization of complex networks with nonhomogeneous Markov jump topology

The problem of synchronization of complex networks with nonhomogeneous Markov jump topology and time-varying coupling delay is investigated in this paper. The Markov process in the underlying complex networks is finite piecewise homogeneous, which is a special case of nonhomogeneous Markov process. Based on the Lyapunov functional approach, an exponential stability condition is derived for the error system in terms of the linear matrix inequality method. Based on the condition, the design method of the desired controller is given. An example is given to show the effectiveness of the proposed technique.     

Delay-dependent robust stability and H_∞ analysis of stochastic systems with time-varying delay

This paper investigates the robust stochastic stability and H∞ analysis for stochastic systems with time-varying delay and Markovian jump. By using the freeweighting matrix technique, i.e., He's technique, and a stochastic Lyapunov-Krasovskii functional, new delay-dependent criteria in terms of linear matrix inequalities are derived for the the robust stochastic stability and the H∞ disturbance attenuation. Three numerical examples are given. The results show that the proposed method is efficient and much less conservative than the existing results in the literature.     

Periodic self-triggered intermittent sampled-data stabilization for stochastic complex networks

In this paper, a new class of periodic self-triggered intermittent control with sampled-data (PSICS) is designed to tackle the stabilization issue for stochastic complex networks with time delays and Lévy noise (SCNTL). Therein, the self-triggered scheme is propounded with regard to intermittent control which is periodic judgment, and there exists a sampled-data control in every intervals of periodic judgment in the control time (work time) of intermittent control. It is worth pointing out that PSICS possesses more flexibility in terms of applications and simpler design of triggered conditions, compared with some previously reported control strategies such as the control combines the advantages of the periodic sampling and self-triggered control. Meanwhile, by means of stability analysis, sampled-data control, intermittent control and event-driven control theory, a useful criterion is established to guarantee the exponential stability in mean square of SCNTL. Notably, the stabilization issue of single-link robot arms with time delays and Lévy noise via PSICS is studied, as a practical application of SCNTL. Ultimately, numerical simulations are utilized for illustration.

     

Exponential stability for switched Cohen–Grossberg neural networks with average dwell time

In this paper, uncertain switched Cohen–Grossberg neural networks with interval time-varying delay and distributed time-varying delay are proposed. Novel multiple Lyapunov functions are employed to investigate the stability of the switched neural networks under the switching rule with the average dwell time property. Sufficient conditions are obtained in terms of linear matrix inequalities (LMIs) which guarantee the exponential stability for the switched Cohen–Grossberg neural networks. Numerical examples are provided to illustrate the effectiveness of the proposed method.