Quantitative and Risk-Based Framework for Unmanned Aircraft Control System Assurance

A generic backtracking process algorithm based on the deductive implementation of a probabilistic Markov/cell-to-cell mapping technique is proposed for risk-informed identification of critical scenarios involving control systems of unmanned aerial systems operating in the National Airspace. A discrete state representation of the unmanned aerial system control system is used that allows tracing fault propagation throughout the system and the quantification of probabilistic system evolution in time. A hybrid state unmanned aerial system control system composed of a discrete state flight executive based on a finite state machine representation of the control system and a continuous state autopilot based on linear control methods is used as a case study. The consequence of interest in the case study is taken to be a deviation from the flight plan at the destination in the waypoint following phase of an unmanned aerial system mission under a “link condition” system configuration. The backtracking process algorithm is implemented to identify the risk-significant scenarios for the case study.