Abstract

Although PID controllers work well on Miniature Air Vehicles (MAVs), they require tuning for each MAV. Also, they quickly lose performance in the presence of actuator failures or changes in the MAV dynamics. Adaptive control algorithms that self tune to each MAV and compensate for changes in the MAV during flight are explored. However, because the autopilots on MAVs are small, many of the adaptive control algorithms like those that employ least squares estimation may take too much code space, memory, and/or computing power. In this thesis we develop several Lyapunov-based model reference adaptive control (MRAC) schemes that are both simple and efficient with the MAV autopilot resources. Most notable are the L1 controllers that have all the benefits of traditional MRACs but have reduced high frequency content to the actuators. The schemes control both roll and pitch through aileron and elevator commands. Flight test results for the schemes are also compared.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

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