Autorotation maneuvers inherently offer little margin for error in execution and induce high pilot workload, particularly as the aircraft nears the ground in an autorotative flare. Control augmentation systems may potentially reduce pilot workload while simultaneously improving the likelihood of a successful landing by offering the pilot appropriate cues. This paper presents an initial investigation of a real-time trajectory generation scheme for autorotative flare based on time-to-contact theory. The algorithm exhibits deterministic runtime performance and provides a speed trajectory that can be tracked by a pilot or inner-loop controller to bring the vehicle to a desired landing point at the time of touchdown. A low-order model of the helicopter dynamics in autorotation is used to evaluate dynamic feasibility of the generated trajectories. By generating and evaluating trajectories to an array of candidate landing points, the set of reachable landing points in front of the aircraft is determined. Simulation results are presented in which the trajectory generator is coupled with a previously derived autorotation controller. Example cases and trade studies are conducted in a six degree-of-freedom simulation environment to demonstrate overall performance as well as robustness of the algorithm to variations in target landing point, helicopter gross weight, and winds. The robustness of the reachability determination portion of the algorithm is likewise evaluated through trade studies examining off-nominal flare entry conditions and the effects of winds.

Autorotation is a challenging flight maneuver that involves highly coordinated control actions and competing constraints. It is generally recognized that pilot performance in autorotation may benefit from additional cues that improve decision-making as well as timing and coordination of control inputs. This paper describes the development and simulator testing of various pilot cues designed for autorotation. A set of discrete and continuous cues are defined to assist in the initiation and execution of various phases of the maneuver. Furthermore, a reachability cue is created to assist a pilot in rapidly evaluating the vehicle glide distance, thereby facilitating selection of a landing site. Piloted simulated flight trials are performed using various combinations of cues in both good and degraded visual environments. Results are evaluated through assessment of pilot workload as well as quantitative measures of landing performance with and without the cues. Overall, results and pilot evaluations show the potential utility of several of the cueing methods but point to specific improvements to them that may facilitate more precise pilot tracking as well as reductions in task workload.

There is increasing demand for full or partial automation of autorotation maneuvers for next-generation helicopters, which may be optionally piloted or capable of fully autonomous flight. A key challenge in the development of autorotation controllers lies in the competing state constraints that often arise during the terminal, or flare, phase of the maneuver. This paper describes the development of a nonlinear model predictive control (NMPC) scheme for autorotation flare. The NMPC controller uses a nonlinear low-order model of the helicopter in autorotation to optimize the sequence of control inputs over a finite horizon. The proposed control scheme offers benefits over existing methods by balancing the simultaneous control objectives of trajectory tracking and rotor speed regulation while also requiring minimal computation time. Simulation results are presented for a six-degree-of-freedom model of the AH-1G aircraft, highlighting the benefits of the model-based control algorithm over a simpler proportional-integral-derivative control scheme. Trade studies and Monte Carlo simulations are presented that quantify the robustness of the controller to varying initial conditions, various target landing distances, and parametric error in the internal low-order model.

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Autorotation is a challenging maneuver during which pilot workload is high. Consequences of an improperly performed maneuver are potentially catastrophic, thus partial automation and/or pilot cueing can potentially be used to reduce pilot workload and increase the probability of a successful landing. This paper describes the development of a nonlinear model predictive control (MPC) scheme and a trajectory generation method that can be used to perform autorotations autonomously, or in development of pilot aids. The proposed control scheme offers potential benefits over existing methods by balancing simultaneous control objectives of trajectory tracking and rotor speed regulation. Results are presented for a six-degree-of-freedom simulation of the AH-1G aircraft. The results are compared to a traditional cascaded PID control scheme to demonstrate the benefits of the MPC algorithm. A trade study is presented in which the target landing point is varied to quantify the benefits of the MPC over a range of landing profiles.