This paper investigates the problem of a scalable distributed state estimation for a class of discrete time-variant systems with state-saturation, quantization effects, and two redundant channels over a sensor network. In transmission data from a sensor to its estimator, two phenomena are considered together. First, the data of each sensor is transmitted to its estimator through two redundant communication channels. Second, innovation data is quantized before being used by the estimator. These phenomena are beneficial in alleviating the negative effects on measurements and reducing the energy consumption and bandwidth. In the structure of proposed filter consensus is used on estimations in which consensus is first achieved on the prediction estimation, then the accuracy of computed estimation is improved by two recursive equations. The parameters of the proposed filter are obtained for each sensor node by employing an upper bound for common error covariance, therefore less computational burden is required. Eventually, the comparative simulation results are presented to show that our method has better performance compared with a rival one recently published.
In this paper, a robust distributed fault estimation is addressed for a class of nonlinear networked systems with actuator faults where influences from process disturbances are minimized. In real‐time monitoring systems, a substantial challenge is to find out the size and the shape of the occurred faults, and consequently, it is significantly more difficult in multi‐agent systems. To consider this concern, in this paper, a distributed fault estimation approach has been proposed for multi‐agent systems such that each agent employs an augmented system owing to a designated communication graph for estimating the fault and states both in this agent and in its neighbors. Meanwhile, each agent is equipped with an unknown input observer (UIO) to decouple the partial disturbances of the process as much as possible and to reduce the disturbances that cannot be decoupled. In the form of linear matrix inequalities (LMIs), sufficient conditions are provided to guarantee stability and to obtain the parameter matrices of the introduced observer. Finally, two numerical examples are provided to verify the performance of the introduced fault estimation scheme.
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