Abstract: 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.
Bio: Mazen Farhood is a Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. His previous positions, before joining Virginia Tech in 2008, include scientific researcher at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands, and postdoctoral fellow at Georgia Tech’s School of Aerospace Engineering. He received the M.S. degree in 2001 and the Ph.D. degree in 2005, both in mechanical engineering from the University of Illinois at Urbana-Champaign. His areas of current research interest include robust control, motion planning and tracking along trajectories, model complexity reduction, and reliability analysis of UAS flight control systems. He received the National Science Foundation CAREER Award in 2014.