Title: System Identification of an Autonomous Aircraft using GPS
Author(s): Jennifer Evans, Gabriel Elkaim, Sherman Lo, Bradford Parkinson
Published in: Proceedings of the 10th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1997)
September 16 - 19, 1997
Kansas City, MO
Pages: 1065 - 1071
Cite this article: Evans, Jennifer, Elkaim, Gabriel, Lo, Sherman, Parkinson, Bradford, "System Identification of an Autonomous Aircraft using GPS," Proceedings of the 10th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1997), Kansas City, MO, September 1997, pp. 1065-1071.
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Abstract: 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 tests 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.