This talk presents a framework based on robust control theory to aid in the certification process of unmanned aircraft system (UAS) flight controllers. Uncertainties are characterized and quantified based on mathematical models and flight test data obtained in-house for a small, commercial, off-the-shelf platform with a custom autopilot. These uncertainties are incorporated via a linear fractional transformation to model the uncertain UAS. Utilizing integral quadratic constraint (IQC) theory to assess the uncertain UAS worst-case performance, it is demonstrated that this framework can determine system sensitivities to uncertainties, compare the robustness of controllers, tune controllers, and indicate when controllers are not sufficiently robust. To ensure repeatability, this framework is used to tune, compare, and analyze a suite of controllers, including path-following, trajectory-tracking, H-infinity, H2, and PID controllers. By employing a non-deterministic simulation environment and conducting numerous flight tests, it is shown that the uncertain UAS framework reliably predicts loss of control, compares the robustness of different controllers, and provides tuned controllers which are sufficiently robust. Furthermore, robust performance guarantees from IQC analysis can be used to provide worst-case bounds on the UAS state at each point in time, providing an inexpensive and robust mathematical tool to aid in the certification of UAS flight controllers.