SUMMARY

Autorotation maneuvers are performed by pilots to safely land after an engine failure in a helicopter. These maneuvers are difficult due to the small window for successful timing, the wide range of possible entry conditions, and the potentially catastrophic consequences of a mistake. This work focuses on automation of the autorotation maneuver and the application of these autonomous algorithms to pilot cues. Specifically, this work is broken into three main categories: Landing Point Tracking and Reachability Determination Near Landing, Model Predictive Control from Maneuver Entry to Touchdown, and a Reachability Determination Pilot Cue Development and Evaluation. The landing point tracking scheme utilizes a biomimetic strategy called Tau-Theory to generate sub-optimal trajectories nearly instantaneously. A point-mass physical model of the helicopter is then used to predict states along an input trajectory. These predicted states can be used to determine the feasibility of the given trajectory. A set of candidate trajectories can be generated and evaluated using these methods to find the set of physically reachable landing points. This set of landing points can be cued to a pilot to aid with choosing a touchdown point. A simply-determined representative reachability footprint will be cued to a pilot in a full-motion simulator to evaluate the effectiveness of such a cue. The Model Predictive Control (MPC) method being developed offers several potential benefits over existing control methods including the capability to intelligently balance state constraints on three outputs using only two control inputs. This multi-input multi-output MPC method solves for the optimal control inputs in closed form using the same point-mass helicopter model employed above. This enables real-time implementation, a necessity for areal vehicles.