Abstract
The study focuses on the modelling and estimation of a class of discrete-time uncertain systems, including network-induced random delays, packet dropouts, and out-of-order packets during the data transmission from the plant to the estimator. In order to improve system performance, event-triggered signal selection method is used to establish the system model. Based on this model, a distributed measurement and centralized fusion estimation scheme is designed using a robust finite horizon Kalman-type filter. Since the phenomena caused by the network-induced deteriorate estimation accuracy, a time-based reorganization measurement is employed to design a linear delay compensation strategy based on estimation. Moreover, in order to obtain the optimal linear estimation, weighted fusion estimation approach is used to perform information collaboration through the error cross-covariance matrix. Simulation results demonstrate that the proposed method has higher estimation performance than the existing methods in this study.
Highlights
The networked system is spatially distributed system based on shared communication technology [1, 2]
A robust adaptive finite-time parameter estimation [18] and control based on parameter estimation errors was studied
Motivated by the above analysis, this study focuses on establishing system model and probing estimator of stochastic time-varying systems
Summary
The networked system is spatially distributed system based on shared communication technology [1, 2]. For uncertain systems, the distributed fusion estimation based on finite horizon filtering are seldom studied This may be due to the difficulty of dealing with the disordered and missing data packets, as well as probing the appropriate upper bounds for filter parameters. For the received measurement with network-induced random transmission delays, packet dropouts, and out-of-order packets, these phenomena are synthetically considered; the system model is established by the signal selection method. In order to effectively discard the out-of-order packets and improve system performance, the system model depending on the event-triggered signal selection method of logic ZOH is established for achieving state estimation. In order to use the event-triggered signal to choose method, Section 3 designs distributed fusion estimation approach based on finite horizon robust Kalman-type filtering.
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