Date of Award

Spring 2021

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Aerospace Engineering

Committee Chair

Dr. Richard Prazenica

First Committee Member

Dr. Hever Moncayo

Second Committee Member

Dr. Kadriye Merve Dogan


Over the years, parameter estimation has focused on approaches in both the time and frequency domains. The parameter estimation process is particularly important for aerospace vehicles that have considerable uncertainty in the model parameters, as might be the case with unmanned aerial vehicles (UAVs). This thesis investigates the use of an Indirect Model Reference Adaptive Controller (MRAC) to provide online, adaptive estimates of uncertain aerodynamic coefficients, which are in turn used in the MRAC to enable an aircraft to track reference trajectories. The performance of the adaptive parameter estimator is compared to that of the Extended Kalman Filter (EKF), a classical time-domain approach. The algorithms will be implemented on simulation models of a general aviation aircraft, which would be representative of the dynamics of a medium-scale fixed-wing UAV. The relative performance of the parameter estimation algorithms within an adaptive control framework is assessed in terms of parameter estimation error and tracking error under various conditions. It was found that limitations exist with the adaptive update laws in terms of number of parameters estimated within the Indirect MRAC system. The Indirect MRAC-EKF was determined to be a viable option to estimate multiple parameters simultaneously.