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A Moving Window Formulation for Recursive Bayesian State Estimation of Systems with Irregularly Sampled and Variable Delays in Measurements
journal contribution
posted on 2014-09-03, 00:00 authored by Vinay
A. Bavdekar, Jagadeesan Prakash, Sachin
C. Patwardhan, Sirish L. ShahThe time delay involved between sampling
and obtaining measurements
of certain quality variables is a common scenario in various process
applications. Further, this delay is not fixed and can vary for various
reasons. Moreover, certain measurements may be sampled at irregular
time intervals. The state estimation algorithms available in the literature
have been developed for the scenario where the measurements are sampled
regularly or are available after a fixed time delay. In this work,
a recursive moving window Bayesian state estimator formulation is
proposed to utilize such measurements with variable time delays to
compute the state estimates. The length of the moving window ensures
that the algorithm utilizes all the available measurements (delayed
or otherwise) for computing the state estimates. In practice, it may
also become necessary to account for the physical bounds on the states.
A constrained version of the moving window recursive state estimator
is also developed to yield state estimates that are consistent with
their respective bounds and constraints. The efficacy of the unconstrained
moving window state estimator is demonstrated by application on the
benchmark Tennessee Eastman simulation case study and an experimental
two-tank heater−mixer setup, while the efficacy of the constrained
moving window state estimator is demonstrated by simulation of a benchmark
gas-phase batch reactor system.