A Comparison of Moving Horizon and Bayesian State Estimators with an Application to a pH Process

Abstract

The moving horizon estimator (MHE) formulation utilizes a window of measurements to compute the estimates of the states in that particular window. This approach leads to smoothing of the state estimates included in the window, since future information is used to compute the same. However, the effect of smoothing, in the MHE algorithm, on the state estimates has not been studied in the literature. In this work the performance of the MHE is compared with recursive Bayesian state estimators (such as UKF, EnKF) to study the effect of the moving window of the past data on the quality of state estimates, via an application on a benchmark pH simulation case study. The simulations are carried out for two scenarios–the ideal case and the case with a parametric model-plant mismatch. The results obtained indicate that the use of MHE results in improved state estimates when compared to the recursive Bayesian state estimators, but does not help compensate for model-plant-mismatch.