A Dynamic Data Driven Approach to Online Flight Envelope Updating for Self Aware Aerospace Vehicles
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abstract
A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. This paper presents a data-driven approach for the online updating of the ight envelope of an unmanned aerial vehicle subjected to structural degradation. In our approach, physics-based models at the panel and vehicle level run offine to generate libraries of information covering a range of damage scenarios. These libraries are queried online to estimate maximum a posteriori vehicle capability states via Bayesian classification using sensed strain data. We demonstrate our methodology on a conceptual unmanned aerial vehicle and focus on standard rate turn maneuvers.
