Barron Associates Work on System Identification Highlighted in Journal of Aircraft Special Edition

Barron Associates was invited to contribute to a special edition on Advances in Aircraft System Identification from Flight Test Data and the special edition is now available (https://arc.aiaa.org/toc/ja/60/5) and has been widely publicized on LinkedIn (https://www.linkedin.com/feed/update/urn:li:activity:7113250384189562881/). Barron’s work, entitled Deterministic and Probabilistic Approaches to Model and Update Dynamic Systems, presents methods to update aircraft models based on deterministic and probabilistic approaches. The deterministic approach is based on a collection of signal processing, statistical analysis, and maximum likelihood estimators embodied in the Algorithms to Update Simulation Parameters with Experimental Data (AUSPEX) tool. Mathematically sound and statistically rigorous techniques are used to calculate updates of current model parameters and suggest new terms capturing unmodeled dynamics, thereby improving correlation with observed vehicle response. The probabilistic approach is based on generalized polynomial chaos theory included in the Algorithms for Uncertainty Representation and Analysis (AURA) tool. The AURA tool allows the user to update probabilistic models based on experimental data, formulated as a Bayesian inference problem.