An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles

• DOI: 10.1016/j.procs.2013.05.365
• Publication Date: 2013-06-01T19:20:31Z
• Authors: D. Allaire; J. Chambers; R. Cowlagi; D. Kordonowy; M. Lecerf; L. Mainini; F. Ulker; K. Willcox
• Keywords: DDDAS; 0209 industrial biotechnology; Bayesian classification; Data library; DDDAS; Computer Science (all); 02 engineering and technology; Bayesian classification; data library

In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such vehicle can dynami- cally adapt the way it performs missions by gathering information about itself its surroundings via sensors responding in- telligently. The key challenge to enabling such is achieve tasks of dynamically autonomously sensing, planning, acting in real time. Our first steps towards achieving goal are presented here, where consider execution mapping strategies from sensed data expected capability while accounting uncertainty. Libraries strain, capability, maneuever loading generated using mission modeling have de- veloped work. These libraries used as part Bayesian classification process estimating state Failure probabilities then computed specific maneuvers. We demonstrate our models methodology on decisions surrounding standard rate turn maneuver.