Date of Award:

2014

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mechanical and Aerospace Engineering

Advisor/Chair:

R. Rees Fullmer

Abstract

Remote sensing has traditionally been done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) provide greater possibilities for personal scientific research than traditional remote sensing platforms. Precision aerial data requires an accurate vehicle dynamics model for controller development, robust flight characteristics, and fault tolerance. One method of developing a model is system identification (system ID). In this thesis system ID of a small low-cost fixed-wing T-tail UAV is conducted. The linerized longitudinal equations of motion are derived from first principles. Foundations of Recursive Least Squares (RLS) are presented along with RLS with an Error Filtering Online Learning scheme (EFOL). Sensors, data collection, data consistency checking, and data processing are described. Batch least squares (BLS) and BLS with EFOL are used to identify aerodynamic coefficients of the UAV. Results of these two methods with flight data are discussed.

Share

COinS