Date of Award
12-2019
Access Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Dr. Richard Prazenica
First Committee Member
Dr. Troy Henderson
Second Committee Member
Dr. 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.
Scholarly Commons Citation
Wilson, Elias, "Autonomous Autorotation of a Tilt-Rotor Aircraft Using Model Predictive Control" (2019). Doctoral Dissertations and Master's Theses. 495.
https://commons.erau.edu/edt/495
