Date of Award
4-22-2021
Access Type
Thesis - Open Access
Degree Name
Master of Science in Engineering Physics
Department
Physical Sciences
Committee Chair
William MacKunis, Ph.D.
First Committee Member
Sergey V. Drakunov, Ph.D.
Second Committee Member
Muhammad Omer Farooq, Ph.D.
Abstract
Wing-embedded synthetic jet actuators (SJA) can be used to achieve maneuvering control in aircraft by delivering controllable airflow perturbations near the wing surface. Trajectory tracking control design for aircraft equipped with SJA is particularly challenging, since the controlling actuator itself has an uncertain dynamic model. These challenges necessitate advanced nonlinear control design methods to achieve desirable performance for SJA-based aircraft (e.g., micro air vehicles (MAVs)). In this research, adaptive and neural-network based control methods are investigated, which are specifically designed to compensate for the SJA dynamic model uncertainty and unpredictable operating conditions characters tic of real-world MAV applications. The control design methods discussed in this thesis are rigorously developed to achieve a prescribed level of trajectory tracking control performance, and numerical simulation results are presented to demonstrate the performance of the controllers in the presence of adversarial operating conditions.
Scholarly Commons Citation
Teramae, Joshua, "Adaptive and Neural Network-Based Aircraft Tracking Control with Synthetic Jet Actuators" (2021). Doctoral Dissertations and Master's Theses. 581.
https://commons.erau.edu/edt/581
