Development of moving window state and parameter estimators under maximum likelihood and Bayesian frameworks

摘要

Highlights?Moving window maximum likelihood and maximum a posteriori state and parameter estimators.?Necessary modifications to handle multi-rate and delayed measurements.?The efficacy of the proposed estimators is demonstrated through the case studies.?The proposed moving window estimators track the drifting parameters fairly accurately.AbstractEstimation of slowly varying model parameters/unmeasured disturbances is of paramount importance in process monitoring, fault diagnosis, model based advanced control and online optimization. The conventional approach to estimate drifting parameters is to artificially model them as a random walk process and estimate them simultaneously with the states. However, this may lead to a poorly conditioned problem, where the tuning of the random walk model becomes a non-trivial exercise. In this work, the moving window parameter estimator of Huang et al. is recast as