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State Estimation of Concentration in CSTR by Extended Kalman Filter

Abstract

Extended Kalman filtering algorithm has been applied to various fields due to its capacity to handle nonlinear/non-Gaussian dynamic problems. The filter is very powerful in several aspects it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modelled system is unknown. Firstly, prediction strategy is used, in which the datas required for filter get predicted. Afterward, correction of errors in predicted datas like instruments error, measurements error will be made. People can flexibly design the filter according to their idiographic requirements. We provide simulation results that show its efficiency and performance..

Ranojidoss R, Sandhya V.P, VamsiKrishnaMuvalla V

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