  We develop a method for estimating the instantaneous lift coefficient on a
rapidly pitching airfoil that uses a small number of pressure sensors and a
measurement of the angle of attack. The approach assimilates four surface
pressure measurements with a modified nonlinear state space model
(Goman-Khrabrov model) through a Kalman filter. The error of lift coefficient
estimates based only on a weighted-sum of the measured pressures are found to
be noisy and biased, which leads to inaccurate estimates. The estimate is
improved by including the predictive model in an conventional Kalman filter.
The Goman-Khrabrov model is shown to be a linear parameter-varying system and
can therefore be used in the Kalman filter without the need for linearization.
Additional improvement is realized by modifying the algorithm to provide more
accurate estimate of the lift coefficient. The improved Kalman filtering
approach results in a bias-free lift coefficient estimate that is more precise
than either the pressure-based estimate or the Goman-Khrabrov model on their
own. The new method will enable performance enhancements in aerodynamic systems
whose performance relies on lift.
