Real-Time Nonlinear Model Predictive Control of a Helicopter in Autorotation

Abstract

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.