Modelling and Estimation for Uncertain Systems with Transmission Delays, Packet Dropouts, and Out‐of‐Order Packets

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.

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

“…Proof. e upper boundary evolution is obtained by minimizing the estimation error covariance matrix, which is similar to solving the filtering parameters in [12,13]. □…”

“…e command governor is formulated as a quadratically constrained quadratic programming problem [11]. Moreover, as opposed to the onestep prediction, multistep prediction is used herein, and a better performance can be achieved [12,13].…”