Modeling, Autopilot Design, and Field Tuning of a UAV With Minimum Control Surfaces

Abstract

While having the benefit of mechanical simplicity, model-scale unmanned aerial vehicles with only two elevon control surfaces present interesting challenges in dynamics modeling, autopilot design, and field tuning. Because of limited on-board computing and communication bandwidth, traditional control theory was applied to systematically tune the proportional-integral-derivative-based (PID) autopilots offline. Based on the aerodynamic analysis, its multi-input, multi-output underactuated linear model configuration was deduced. Utilizing the real-time flight data collected from human-controlled test flight, a two-input three-output linear model was obtained by means of system identification. It includes the transfer functions in the airspeed loop, heading loop, and altitude loop. The dynamic behavior of the aircraft was analyzed, and five PID controllers in three loops were designed based on the root-locus techniques. The controllers were implemented and further tuned in field flights with improved performances. We demonstrate that with proper precautions, traditional control theory can be used to solve complex control problems that are often tackled with nonlinear control algorithms.