State estimation of Markov jump neural networks with random delays by redundant channels

Abstract This paper deals with the problem of state estimation for Markov jump neural networks with random delays through redundant channels. First, a redundant channel is provided to increase the probability of successfully transmitting the measurements over the shared communication network, and two mutually independent Bernoulli sequences are used to reflect the data dropouts phenomena in the main channel and the redundant channel. In addition, the time-varying random delay is both mode-dependent and distribution-dependent, and its probabilistic characteristic obeys Bernoulli distribution. Then, by constructing a suitable Lyapunov-Krasovskii functional, the sufficient condition is obtained to guarantee the augmented estimation error system is mean-square stable with a prescribed H ∞ disturbance attenuation performance. Meanwhile, a mode-dependent estimator is designed by convex optimization method. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.