Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making

This paper proposes a data-driven strategy to assist online rapid decision making for an unmanned aerial vehicle that uses sensed data to estimate its structural state, uses this estimate to update its corresponding flight capabilities, and then dynamically replans its mission accordingly. The approach comprises offline and online computational phases constructed to address the sense-plan-act information flow while avoiding a costly online inference step. During the offline phase, high-fidelity finite element simulations are used to construct reduced-order models and classification criteria: proper orthogonal decomposition approximations and self-organizing maps are combined to realize a fast mapping from measured quantities to system capabilities. During the online phase, the surrogate mapping is employed to directly estimate the vehicle's evolving structural capability from sensor data. The approach is demonstrated for a test problem of a composite wing panel on an unmanned aerial vehicle that undergoes degradation in structural properties.