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Bayesian Hybrid State Estimation for Unequal-length Batch Processes with Incomplete Observations
Guoli Ji,Yaozong Wang,Shunyi Zhao,Yunlong Liu,Kangkang Zhang,Bin Yao,Sun Zhou 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.6
This paper investigates state estimation problem for batch processes with unequal-length batches as wellas incomplete observations. A Bayesian hybrid state estimation method is proposed based on two dimensional (2D)correlations of states. The states of equal-length segment of time are estimated according to both within-a-batchand batch-to-batch correlations, and the states of unequal-length segment are obtained according to the correlationswithin the batch. In this way, the batch process states can be achieved in both equal-length and unequal-lengthsituations, of which the latter one is a more general case. In order to approximate state distribution of nonlinearsystem and to deal with the problem of incomplete observations, particle filter (PF) is employed. The proposedmethod shows its superiority with a nonlinear system and a gas-phase reaction process. Compared to a typicalexisting method, the proposed method provides better estimation accuracy in the situation of equal-length batches,also it shows less sensitivity to incomplete observations.