Date of Award

Fall 12-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mechanical & Aerospace Engineering

Program/Concentration

Aerospace Engineering

Committee Director

Drew Landman

Committee Member

Thomas Alberts

Committee Member

Loc Tran

Committee Member

Gene Hou

Abstract

Traditionally fixed-wing small Unmanned Arial Vehicles (sUAV) are flown while in direct line of sight with commands from a remote operator. However, this is changing with the increased popularity and ready availability of low-cost flight controllers. Flight controllers provide fixed-wing sUAVs with functions that either minimize or eliminate the need for a remote operator. Since the remote operator is no longer controlling the sUAV, it is impossible to determine if the fixed-wing sUAV has proper control authority. In this work, a controllability detection system was designed, built, and flight-tested using COTS hardware. The method features in-situ measurement and analysis of the angular velocity response for the roll, pitch, and yaw axis using a Multi-Input Multi-Output (MIMO) Autoregressive with Exogenous input (ARX) modeling technique. The method is structured so that no prior knowledge of the airplane dimensions, control surface deflection angles, mass, or moment of inertia are required. The diagnostic is performed in flight with no post-processing so that controllability may be assessed during normal operations. This diagnostic works by comparison of baseline healthy control responses to current responses using statistical analysis. The outcome of this work shows that this is a viable way to check for degraded control authority.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/jn8n-kk21

ISBN

9798557044387

Download

Share

COinS