Dynamic Data Driven Methods for Self-aware Aerospace Vehicles.

摘要

This project aimed to develop novel inference approaches for dynamic vehicle state estimation and methods for online management of multifidelity models and sensor data, and to apply the new methods to quantify the benefits of a self-aware unmanned aerial vehicle (UAV) in terms of reliability, maneuverability and survivability. The project accomplished all objectives and resulted in the development of new DDDAS methodology and DDDAS algorithms, new models for a DDDAS-enabled self-aware UAV, and a demonstration of the value of DDDAS in the context of dynamic data-driven structural assessment to support decision-making for a damaged vehicle taking evasive action in a hostile environment.