Developing a Certifiable UAS reliability Assessment Approach Through Algorithmic Redundancy

Integration of Unmanned Aerial Systems (UAS) into the US National Airspace (NAS) is crucial for advancement of UAS applications in different fields like transport, surveillance, disaster management, and geospatial applications. Airworthiness certification by the Federal Aviation Administration (FAA) is a critical step towards NAS integration. To match the expected FAA safety and reliability standards for UAS certification, novel technologies are needed. Conventional manned air vehicles meet FAA standards via hardware redundancy, an option unavailable for UAS due to size, weight, and power constraints. This challenge was addressed in Phase I via the so-called algorithmically redundant approach, where multiple fault detection and isolation algorithms work in parallel to detect faults more reliably. Using FAA-approved procedures like fault tree analysis or failure modes and effects analysis, reliability of algorithmically redundant systems is computed. This approach will be extended in Phase II to include flight control system hardware and control laws in the loop, and will involve validation via flight testing. A comprehensive methodology and accompanying software toolbox will result: STI Approach for Fault Estimation and Reliability for UAS (SAFER-UAS). SAFER-UAS is an overall reliability estimation approach based on theoretical analysis, simulation runs, and flight tests, serving as a vital step toward certification.