Evaluation of Adaptive Extended Kalman Filter Algorithms for State Estimation in Presence of Model-Plant Mismatch

The occurrence of model-plant mismatch is a common problem in dynamic model based applications such as state estimation. The use of an inaccurate model results in biased estimates of the states. Hence, conventional state estimation algorithms are modified in various ways to compensate for model-plan...

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Bibliographic Details
Published inIFAC Proceedings Volumes Vol. 46; no. 32; pp. 184 - 189
Main Authors Bavdekar, Vinay A, Bhushan Gopaluni, R., Shah, Sirish L
Format Journal Article
LanguageEnglish
Published 01.12.2013
Subjects
adaptive state estimation
extended Kalman filter
model-plant mismatch
state and parameter estimation
adaptive state estimation
model-plant mismatch
state and parameter estimation
extended Kalman filter
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Summary:The occurrence of model-plant mismatch is a common problem in dynamic model based applications such as state estimation. The use of an inaccurate model results in biased estimates of the states. Hence, conventional state estimation algorithms are modified in various ways to compensate for model-plant mismatch. In this work, the performance of four adaptive state estimation algorithms is compared in the presence of a model plant mismatch arising due to random drifts in parameter values. The comparison is carried out through simulations on a benchmark non-isothermal CSTR problem. Simulation results demonstrate that online re-identification of the parameters susceptible to drift or change is the most effective approach to minimize the effect of model-plant mismatch on the state estimates.
ISSN:1474-6670
DOI:10.3182/20131218-3-IN-2045.00175