Date of Award

Fall 12-2019

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Richard Prazenica

First Committee Member

Troy Henderson

Second Committee Member

Morad Nazari

Abstract

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.

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