This paper presents a system identification procedure for a class of small, rudderless, fixed-wing unmanned aircraft. The procedure is demonstrated on an aircraft that is equipped with only two aerodynamic control surfaces (called elevons) and one electric motor. A physics-based, first-principles approach is used to obtain the initial model parameters. The initial model is used to design flight tests wherein the longitudinal and the lateral-directional dynamics are separately excited. The aircraft is rudderless and this introduces a key challenge in the model identification. Specifically, the lateral-directional model has more free parameters than can be identified using the elevon excitations alone. This paper resorts to two novel steps to navigate this roadblock. First, this paper uses black-box methods to identify sensitive modes whose damping ratios and natural frequencies change significantly compared with their initial values. Second, gray-box methods are used to update the stability and control derivatives related to these sensitive modes, while retaining the remaining derivatives at their respective initial values. Additional flight tests are conducted to validate the updated model parameters.
Bibliographical noteFunding Information:
This work was supported by the National Science Foundation under Grant No. NSF/CNS-1329390 entitled “CPS: Breakthrough: Collaborative Research: Managing Uncertainty in the Design of Safety-Critical Aviation Systems.” The first author acknowledges financial support from the University of Minnesota through the 2017-2018 Doctoral Dissertation Fellowship. The authors thank the following individuals: T. Colten of Sentera for donating the Vireo aircraft; C. Olson for the flight software, excitation signal implementation, and piloting; and N. Carter, R. Condron, L. Heide, A. Mahon, C. Regan, and B. Taylor for the aircraft integration and testing.