Real-time predictions of vehicle capabilities for reconfigurable mission planning

Reliability and operational availability of unmanned vehicles can be augmented through a dynamic reshaping of their operational and mission profile in response to the evolution of their health state and contingencies. In hazardous settings, the dynamic reconfiguration of a mission profile requires real-time predictions of residual capabilities which determine the set of feasible manoeuvres to preserve the vehicle and complete the mission successfully. This work discusses two computational frameworks to predict system capabilities from on-board sensor measurements and actualize a form of self-awareness for unmanned air vehicles in support of reconfigurable mission planning. The first framework relies on a traditional approach to diagnostics and prognostics: model reduction and supervised learning are combined to accelerate both the identification of damage parameters and the prediction of system capabilities. The second framework introduces a priority shift that emphasizes the prediction of vehicle capabilities over the characterization of the damage: an original bypass scheme (named MultiStep-ROM) combines projection-based model reduction and unsupervised machine learning into a form of transfer learning that computes adaptive models directly mapping measurements into capabilities. The two approaches are presented through the example cases of unmanned air vehicles that undergo failures of on-board actuation devices and structural damages. The computational experiments indicate that the bypass approach allows to obtain sensitively faster predictions of vehicle capabilities and is better suited to meet real-time responsiveness requirements than the traditional scheme.