Files in this item



application/pdfSUN_YUE.pdf (4MB)
(no description provided)PDF


Title:Modeling, identification and control of a quad-rotor drone using low-resolution sensing
Author(s):Sun, Yue
Advisor(s):Dullerud, Geir E.
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Abstract:This thesis focuses on the modeling and identification, control and filter design, simulation and animation, and experiments of an electrical-motor drive model-scale quadrotor --- the AR.Drone. Equations of Motion of drone’s model were derived from Kinemics and Dynamics of common quadrotors. The identification was conducted thoroughly including its low-resolution on-board sensors, such as rate gyro and altimeter. Control targets are composed of two stages --- local references following and global position tracking. PID algorithm is used by both controllers with various filters designs, such as low/high pass filter, Complementary Filter and Kalman Filter. Simulation is also divided to two stages with two different simulators ---- MATLAB and C++. The first stage MATLAB simulation is intended to only test the controllers with no disturbances or noises. The second stage high fidelity C++ simulation contains everything including animation. Experiments results are presented and correlated to simulation to evaluate the identification and modeling. This thesis also includes modeling and identification of a low-resolution camera sensor --- Kinect. The model is included in global position tracking simulation. Some experiments videos and animation videos are available at The author hopes this thesis is helpful to researchers and amateurs who would like to develop the AR.Drone or any other small scale quadrotors using low-resolution sensing for autonomous control.
Issue Date:2012-09-18
Rights Information:Copyright 2012 Yue Sun
Date Available in IDEALS:2012-09-18
Date Deposited:2012-08

This item appears in the following Collection(s)

Item Statistics