Tilt rotor vehicles are governed by FAA laws also used for conventional helicopters, which require autorotational maneuvering and landing given a total power failure. With low inertia rotors and high disk loading of tilt rotor vehicles, this already difficult task becomes significantly more challenging. In this work, a model predictive controller is developed to autonomously maneuver and land a tilt rotor given complete power loss. A high fidelity model of a tilt rotor vehicle is created and used to simulate the vehicle dynamics and response to control inputs. A reduced order dynamic model is used within a model predictive control algorithm to predict vehicle states on a receding horizon and optimize the control inputs. Constraint and cost functions are designed to promote reliable nonlinear optimization using a recurrent neural network. Simulation results show that the controller works in both normal operation states and in power-off autorotation
