This work considers distributed moving horizon state estimation of nonlinear systems subject to communication delays and data losses. In the proposed design, a local estimator is designed for each subsystem and the distributed estimators communicate to collaborate. Within each local estimator, an open-loop predictor is embedded. A two-step prediction-update strategy is used in the predictor design in order to handle delays and data losses simultaneously. Based on the predictions provided by the predictor, an auxiliary nonlinear observer is taken advantage of to calculate a reference subsystem state estimate based upon which a confidence region of the state of the subsystem is generated. Within the confidence region, the local estimator optimizes its subsystem state estimate. Sufficient conditions under which the proposed design gives decreasing and ultimately bounded estimation error are provided. The effectiveness of the proposed approach is illustrated via the application to a chemical process example.
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