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This paper investigates the finite-time leader-following consensus problem for multi-agent systems with switching topologies. First of all, consider the characteristic of failures, a failure distribution model which is more in line with the actual situation is established, and a semi-Markov chain is used to describe the topology switching of the multi-agent system, in which the transfer rate is time-varying. Secondly, based on the past state information, a topology-dependent consensus protocol is established, then the fault-tolerant consensus problem of leader-following multi-agent is transformed into the issue of system stability, according to the mode-dependent Lyapunov function and the finite time stochastic theorem, the sufficient conditions for the stability of the closed-loop system and the constraints of average residence time are obtained. In addition, an initial-state based $ {H_\infty } $ H ∞ performance index is proposed to reduce the influence of zero initial conditions on the system. Finally, a numerical example is provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the controller design problem for mean-square asymptotical synchronization of discrete-time neural networks with time-varying delay. We proposed the design method of synchronization controller, which considered the nonlinearity of controller input. Based on the designed controller, a delay-dependent synchronization criterion is proposed and formulated in the form of linear matrix inequalities (LMIs) by applying the Lyapunov function method. The result is extended to the delayed discrete-time neural network with uncertainty. Two numerical examples are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the mean-square exponential stability problem for discrete-time recurrent neural networks with time-varying discrete and distributed delays is investigated. Considering the delay distributions, a novel class of Lyapunov functional is introduced. By exploiting all possible information in mixed time delays, a sufficient condition for the whole system to be mean-square exponentially stable is given. Numerical examples are proposed to illustrate the effectiveness of the method, and show that by using the approach in this paper, the obtained results are less conservative than the existing ones. [ABSTRACT FROM AUTHOR]

A new stochastic switched linear model is established to describe the Zigbee-based wireless networked control system (WNCS) with both network-induced delay and packet dropout. The network-induced delay can be less or longer than one sampling period. A sufficient condition is presented for the exponentially mean square stability of the closed-loop WNCS, and corresponding state feedback controller is designed by using the augmenting technique and multi-Lyapunov approach. Then, combined with carrier sense multiple access with collision avoidance (CSMA-CA) algorithm, a method is given to choose proper parameter values. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of robust stabilisation for uncertain discrete singular systems with time-varying delay is discussed in this article. First, a new delay-distribution-dependent Lyapunov function is established. Then, sufficient conditions are proposed for guaranteeing that the uncertain discrete singular system with time-varying delay has the properties of regularity, causality and stability. Finally, a suitable robust state feedback controller is designed by linear matrix inequality. Numerical examples illustrate that the results derived from the proposed method are less conservative than the existing ones. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis problem for uncertain stochastic discrete-time recurrent neural networks with time-varying delay. By using linear matrix inequality method and discrete Jensen inequality, a new Lyapunov–Krasovskii function is established to derive sufficient condition for globally asymptotical stability in mean square of the recurrent neural networks with stochastic disturbance. As an extension, we further consider the stability analysis problem for the same class of neural networks but with uncertainty. It is shown that the newly obtained result is less conservative than the existing ones when the described system is without disturbance and uncertainty. Meanwhile, the computational complexity is reduced since less variable is involved. Two numerical examples are presented to illustrate the effectiveness and the benefits of the proposed method. [ABSTRACT FROM AUTHOR]