Distributed state estimation from infrequent and delayed measurements in large‐scale processes

Abstract Effective control and monitoring of a process usually require frequent and delay‐free measurements of important process output variables. However, these measurements are often either not available or available infrequently with significant time delays. This article presents a method that allows for improving the performance of distributed state estimators implemented on large‐scale manufacturing processes. The method uses a sample state augmentation approach that permits using delayed measurements in distributed state estimation. The method can be used with any state estimator, including unscented Kalman filters, extended Kalman filters, and moving horizon state estimators. The method optimally handles the tradeoff between computational time and estimation accuracy in distributed state estimation implemented using a computer with parallel processors. Its implementation and performance are shown using a few simulated examples.