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 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.