Papers by Keyword: Time-Varying Delay

Abstract: This paper investigates the exponential stability problem for a class of stochastic neural networks with leakage delay. By employing a suitable Lyapunov functional and stochastic stability theory technic, the sufficient conditions which make the stochastic neural networks system exponential mean square stable are proposed and proved. All results are expressed in terms of linear matrix inequalities (LMIs). Example and simulation are presented to show the effectiveness of the proposed method.

Abstract: A new method for constructing a Lyapunov-Razumikhn function to deal with the stability problem of time-varying delay nonlinear uncertain system is presented in this paper. A quadratic trinomial with two variables（ξ,Τ）is obtained, and then the upper bound of the allowable delay Τ can be obtained by solving the optimization problem with varying positive matrix Q. That is to say, we can obtain the optimal combination of Τ and Q matrix.

Abstract: In this paper, we discuss consensus problems for a network of dynamic agents with fixed topologies. A consensus algorithm for multi-agent systems with time-varying delay is presented. With the consideration of the effects of network conditions, such as network-induced delays, packet dropouts, mis-sequence, etc., a communication buffer is introduced for avoiding agents’ communication error, and a method for choosing buffer length is proposed. Using this buffer design, received data is rearranged and transferred in the original order. With the presented consensus algorithm, agents’ consensus is well-performance, and all the agents match the average speed finally. Simulation oriented results verify the effectiveness of the proposed algorithm.

Abstract: The sliding mode control (SMC) design is discussed for a class of time-varying delay systems which is delay-range-dependent and rate-range-dependent. A novel time-varying nonlinear sliding surface is employed. The choice of nonlinear sliding surface is to change the state matrix of sliding mode system, which can combine the advantages of different conventional linear sliding surfaces. Thus the better transient qualities of system states, i.e., quicker response and smaller overshoot, can be achieved. The sufficient conditions ensuring the asymptotic stability of sliding mode are derived in terms of linear matrix inequalities. The algorithms deciding unknown parameters of the nonlinear sliding surface and the corresponding sliding mode controller are also presented. Finally, A numerical example is given to illustrate the effectiveness of the result here.

Abstract: Back propagation (BP) neural network is used to approximate the dynamic character of nonlinear discrete-time system. Considering the unmodeling dynamics of the system, the weights of neural network are updated by using a dead-zone algorithm and a robust adaptive controller based on the BP neural network is proposed. For the situation that jumping change parameters exist, multiple neural networks with multiple weights are built to cover the uncertainty of parameters, and multiple controllers based on these models are set up. At every sample time, a performance index function based on the identification error will be used to choose the optimal model and the corresponding controller. Different kinds of combinations of fixed model and adaptive model will be used for robust multiple models adaptive control (MMAC). The proof of stability and convergence of MMAC are given, and the significant efficacy of the proposed methods is tested by simulation. This paper deals with the problem of delay-dependent stability criterion of delay-difference system with multiple delays of cellular neural networks. Based on quadratic Lyapunov functional approach and free-weighting matrix approach, some linear matrix inequality criteria are found to guarantee delay-dependent asymptotical stability of these systems. And one example illustrates the exactness of the proposed criteria.

Abstract: In this paper, the problem of the exponentially stable sampled-data control was investigated for a class of uncertain systems. Based on the input delay approach, the system was modeled as a continuous-time system with the delayed control input. Attention was focused on the design of a state feedback sampled-data controller which guarantees the exponential stability of the closed-loop system for all admissible parametric uncertainties. Using linear matrix inequality (LMI) approach, sufficient conditions are obtained. Simulation example was given to demonstrate the effectiveness and correctness of the proposed method.

Abstract: This paper presents an adaptive robust controller for discrete-time systems with time-varying uncertainty and time-varying delay. The controller is designed based on the online parameter estimation and robust H∞ approach. Simulation result is given to verify the effectiveness of the proposed controller.

Abstract: This paper study the problem of the stability analysis for Lurie type systems with time-varying delay. By exploiting a suitable Lyapunov-Krasovskii functional, new criteria on delay-dependent stability are derived. Some better results are obtained by using a delay partitioning approach and reciprocally convex technique. Finally, numerical examples are presented to illustrate the effectiveness and reduced conservatism of the obtain results.

Abstract: This paper is concerned with the mean-square exponential stability analysis problem for a class of stochastic interval cellular neural networks with time-varying delay. By using the stochastic analysis approach, employing Lyapunov function and norm inequalities, several mean-square exponential stability criteria are established in terms of the formula and Razumikhin theorem to guarantee the stochastic interval delayed cellular neural networks to be mean-square exponential stable. Some recent results reported in the literatures are generalized. A kind of equivalent description for this stochastic interval cellular neural networks with time-varying delay is also given.

Abstract: Network control system (NCS) is developed from the cross-integration of network technology and control technology, which is with advantages of sharing resources, low price, light weight, easy installation and maintenance, low energy consumption and so on. A robust control approach is proposed in this paper to solve the stabilization problem for networked control systems (NCS) with short time-varying delays. By considering state feedback controllers, the closed-loop NCS is described as a discrete-time linear uncertain system model. Then, the asymptotic stability condition for the obtained closed-loop NCS is derived, which establishes the quantitative relation between the stability of the closed-loop NCS and two delay parameters, namely, the allowable delay upper bound (ADB) and the allowable delay variation range (ADVR). Furthermore, design procedures for the stabilizing controllers are also presented. An illustrative example is finally given to demonstrate the effectiveness of the proposed method.