System identification for a small, rudderless, fixed-wing unmanned aircraft

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.