Estimating Loss-of-Control: a Data-Based Predictive Control Approach

Loss-of-control is a major contributor to aircraft fatalities. Recent work has been done to develop quantitative criteria for determining loss-of-control from accident time history data. This work proposes an approach to estimating boundaries on control actions to provide information to pilots and/or control systems to assist in avoiding loss-of-control scenarios. Data-based predictive control theory is used to develop an algorithm that finds the minimum control input that will result in the aircraft exceeding a safe operating envelope at various minimum time estimates. The calculated minimum control inputs become a boundary of a set of safe control inputs. With this information, a pilot could change flying strategy or an autonomous system could schedule controller gains to prevent the vehicle from exceeding the envelope.