No abstract

No abstract

In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.

No abstract

No abstract

Abstract-This paper studies the problem of passivity-based asynchronous control for discrete-time Markov jump systems.The asynchronization phenomenon appears between the system modes and controller modes, which is described by a hidden Markov model. Accordingly, the resultant closed-loop system is named as a hidden Markov jump system. By utilizing the matrix inequality technique, three equivalent sufficient conditions are proposed to ensure the stochastic passivity of the hidden Markov jump systems. Based on the established conditions, the design of asynchronous controller, which covers synchronous controller and mode-independent controller as special cases, is addressed. A numerical example is given to demonstrate the effectiveness of the derived results.

No abstract

SUMMARYThe problem of H ∞ filtering is considered for singular Markovian jump systems with time delay. In terms of linear matrix inequality (LMI) approach, a delay-dependent bounded real lemma (BRL) is proposed for the considered system to be stochastically admissible while achieving the prescribed H ∞ performance condition. Based on the BRL and under partial knowledge of the jump rates of the Markov process, both delay-dependent and delay-independent sufficient conditions that guarantee the existence of the desired filter are presented. The explicit expression of the desired filter gains is also characterized by solving a set of strict LMIs. Some numerical examples are given to demonstrate the effectiveness of the proposed methods.