A Flexible Wing Model Uncertainty Evaluation Based on an Autocovariance Least Squares Tuned Optimal Estimate

A Kullback-Leibler (K-L) divergence based metric is proposed to compare the performance of a set of linear Kalman Filters for a flexible wing wind tunnel model. Each of the Kalman Filters is designed with the Autocovariance Least Squares (ALS) technique based on common data sets but different state-space models. The metric is used to identify areas of model uncertainty and informs selection of the best model. The method is demonstrated both for simulation of a simple mass-spring-damper system and for a gust load alleviation test- bed in the commercial Kirsten Wind Tunnel facility.