Adaptive and Neural Network-Based Aircraft Tracking Control with Synthetic Jet Actuators

<p>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.</p>

Name thesis:degree_name

Master of Science in Engineering Physics

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Physical Sciences

Year

2021

Author dc:creator

• Teramae, Joshua

• synthetic jet actuators
• tracking
• aircraft
• adaptive control
• Navigation, Guidance, Control and Dynamics

Repository record dc:identifier

https://commons.erau.edu/edt/581

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1588