Date of Award:

5-2026

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mechanical and Aerospace Engineering

Committee Chair(s)

Douglas Hunsaker

Committee

Douglas Hunsaker

Committee

Tianyi He

Committee

Greg Droge

Abstract

The physical motion of an aircraft is determined by the resulting forces and moments acting on the aircraft. These forces and moments can be defined as a mathematical equation where the control deflections, rotation rates, and orientation of the aircraft are multiplied by individual constants and summed together to define the entire force or moment. This method results in a highly accurate model that defines the motion of the aircraft at low angles of attack and sideslip. Initial calculations of the aerodynamic model are created based on analytical estimations based on the physical properties of the aircraft. Additional refinement of the model is achieved in flight testing. During flight tests, the physical states of the aircraft, such as orientation, translational motion, and rotational motion, are recorded as well as the input commands for the aircraft. An analysis of the input commands and the output states provides the necessary data to define the aerodynamic model of the aircraft. This method of defining the motion of an object based on the input commands and the output states is called system identification. The aerodynamic model resulting from the system identification method is highly dependent on the accuracy of the input and output parameters as this is the only available information in the process. This high dependence demands a robust method to analyze and manage the error that is inherent in measurements. Most research in the area of error analysis in system identification focuses on removal or management of error. This research analyzes how much effect individual sensor error and assumptions have on the resulting aerodynamic model. This research identifies a robust maneuver input that provides sufficient measurement data for the system identification method. A best-case scenario will be tested in a simulation environment to determine what accuracy is possible with the specific maneuver case. The simulation environment provides direct control of introduced errors and assumptions, providing a platform to test the influence of each error and assumption on the resulting aerodynamic model. The results show that model assumptions need to be carefully analyzed to understand the effect on the resulting aerodynamic model. The assumption that the velocity of the aircraft can be simplified to be the forward velocity, which is the velocity measured by the pitot tube, is an erroneous assumption and highly corrupts the result. The second result that introduced significant error is using the commanded control inputs instead of the actual physical position of the control surfaces. This analysis shows the need to accurately measure or estimate the full velocity vector and the true control surface deflections. The results can provide practical guidance for designing a successful flight test, helping engineers determine what sensors are necessary and which assumptions must be avoided to build a flight test platform that can successfully find the aerodynamic model through system identification.

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