System identification of an autonomous aircraft using GPS

Stanford University's GPS Laboratory has developed and demonstrated a fully autonomous, small, unmanned airplane. Recent flight tests of the airplane have been extended to collect appropriate open-loop data to perform system identification. In previous research in the GPS Lab, the autonomous airplane, utilizing Carrier-Phase Differential GPS (CDGPS), has flown several flights of a predetermined trajectory from take-off to landing. GPS, providing position, velocity, attitude, and attitude rate, was the primary sensor for the automatic controller. No inertial sensors were used during the autonomous flights. The only additional sensors for these previous flights were indicators for wind speed and direction. Carrier Phase Differential GPS was the enabling technology for the autonomous control. In earlier flight tests, the low noise, high bandwidth, precise positioning allowed the controller to function well with full sensor feedback. In fact, sensor performance was accurate enough to allow the controller to perform well even without an elaborate mathematical system model of the aircraft. Previous flight test demonstrated a total system error of typically less than 0.5 m. The same low noise, high bandwidth qualities of the GPS position and attitude system make it ideal for system identification. The multiple vehicle state information is collected and used to generate a mathematical model of the airplane. During the recent flight tests, the control surfaces are systematically disturbed to observe the aircraft modes. Several different modeling techniques are applied to the same data and results are compared. Standard aircraft modeling techniques using parameter identification and a priori knowledge of linearized dynamics are compared to techniques assuming no a priori information.