Nonlinear time constant estimation and dynamic compensation of temperature sensors

The problem of dynamic sensor compensation is considered. A local linear model and a physical sensor model are proposed for estimation of nonlinear sensor time constants, both taking into account the nonlinear dependency of the sensor time constant on gas velocity or mass flow. The parameter estimat...

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Bibliographic Details
Published inControl engineering practice Vol. 18; no. 3; pp. 300 - 310
Main Authors Zimmerschied, Ralf, Isermann, Rolf
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.03.2010
Elsevier
Subjects
Applied sciences
Automatic offset compensation
Computer science; control theory; systems
Control theory. Systems
Dynamic sensor compensation
Exact sciences and technology
Extended Kalman filter
Local linear model
Modelling and identification
Orthogonal distance regression
Physical sensor model
Total least squares
Orthogonal distance regression
Dynamic sensor compensation
Total least squares
Local linear model
Physical sensor model
Automatic offset compensation
Extended Kalman filter
Parameter estimation
dynamic control
Modeling
Gas flow
Physical model
Least squares method
Input estimation
System identification
Time constant
Measurement sensor
Temperature measurement
Thermal compensation
Variable parameter
Flow velocity
Error correction
Gas detector
Mass flow
Signal to noise ratio
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Summary:The problem of dynamic sensor compensation is considered. A local linear model and a physical sensor model are proposed for estimation of nonlinear sensor time constants, both taking into account the nonlinear dependency of the sensor time constant on gas velocity or mass flow. The parameter estimation is performed using total least squares and orthogonal distance regression to cope with the noise present on the regressors and the output. Furthermore, offset errors on the sensors are automatically compensated. An extended Kalman filter is proposed to reconstruct the sensor input where the noise amplification due to the inverse filtering is reduced by a variable filter parameter. Two sensors having different time constants are necessary for the time constant estimation, whereas just one sensor is required for the dynamic input reconstruction.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2009.11.008