Distributed state and fault estimation over sensor networks with probabilistic quantizations: The dynamic event-triggered case

Abstract

In this paper, the distributed state and fault estimation problem is discussed for a class of nonlinear time-varying systems with probabilistic quantizations and dynamic event-triggered mechanisms. To reduce resource consumption, a dynamic event-triggered strategy is exploited to schedule the data communication among sensor nodes. In addition, the measurement signals are quantized and then transmitted through the network, where the probabilistic quantizations are taken into consideration. Attention is focused on the problem of constructing a distributed estimator such that both the plant state and the fault signal are estimated simultaneously. By using the matrix difference equation method, certain upper bound on the estimation error covariance is first guaranteed and then minimized at each iteration by properly designing the estimator parameters. Subsequently, for the proposed distributed estimation algorithm, the estimator performance is analyzed and a sufficient condition is established to guarantee that the estimation error is exponentially bounded in mean-square sense. Finally, an illustrative example is provided to verify the usefulness of the developed estimation scheme.