scholarly journals Non-linear State Estimator for the On-line Control of a Sinter Plant

2013 ◽  
Vol 53 (9) ◽  
pp. 1658-1664 ◽  
Author(s):  
Jesús Saiz ◽  
Maria José Posada
2020 ◽  
Vol 161 ◽  
pp. 676-690 ◽  
Author(s):  
R. Pili ◽  
S. Eyerer ◽  
F. Dawo ◽  
C. Wieland ◽  
H. Spliethoff

2013 ◽  
Vol 23 (4) ◽  
pp. 516-526 ◽  
Author(s):  
Aditya Tulsyan ◽  
Biao Huang ◽  
R. Bhushan Gopaluni ◽  
J. Fraser Forbes

Author(s):  
Héctor Botero ◽  
Hernán Álvarez

This paper proposes a new composite observer capable of estimating the states and unknown (or changing) parameters of a chemical process, using some input-output measurements, the phenomenological based model and other available knowledge about the process. The proposed composite observer contains a classic observer (CO) to estimate the state variables, an observer-based estimator (OBE) to obtain the actual values of the unknown or changing parameters needed to tune the CO, and an asymptotic observer (AO) to estimate the states needed as input to the OBE. The proposed structure was applied to a CSTR model with three state variables. With the proposed structure, the concentration of reactants and other CSTR parameters can be estimated on-line if the reactor and jacket temperatures are known. The procedure for the design of the proposed structure is simple and guarantees observer convergence. In addition, the convergence speed of state and parameter estimation can be adjusted independently.


1990 ◽  
Vol 112 (4) ◽  
pp. 774-781 ◽  
Author(s):  
R. J. Chang

A practical technique to derive a discrete-time linear state estimator for estimating the states of a nonlinearizable stochastic system involving both state-dependent and external noises through a linear noisy measurement system is presented. The present technique for synthesizing a discrete-time linear state estimator is first to construct an equivalent reference linear model for the nonlinearizable system such that the equivalent model will provide the same stationary covariance response as that of the nonlinear system. From the linear continuous model, a discrete-time state estimator can be directly derived from the corresponding discrete-time model. The synthesizing technique and filtering performance are illustrated and simulated by selecting linear, linearizable, and nonlinearizable systems with state-dependent noise.


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