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Frequency domain system identification of higher order models for the Bell 412 helicopter was performed. First, a frequency response database was derived from flight-test data. For hover, a combination of sweep and 2311-multistep maneuvers had to be used to achieve good results. In addition to the classical six-DoF (degrees of freedom) rigid body states, the identified hover model includes dynamic inflow, rotor coning dynamics, and uses a Padé approximation for the influence of engine dynamics, to improve the response in the vertical axis. The forward flight (60 kn) model includes as extension first-order flapping dynamics, mainly to improve the roll and pitch response. Besides the simple Padé approach used in the hover model, two different engine model structures were investigated but they provided no significant improvement compared to the Padé solution when coupled to the rigid-body model. Finally, a method derived from feedforward principles of model following control is shown, to use the identified hover model to analytically derive an “input filter” correction that improves the fidelity of a linearized FLIGHTLAB simulation model.

Aircraft system identification has a five-decades-long tradition at German Aerospace Center (DLR). Over the last two decades, the research covered various topics related to system identification of fixed- and rotary-wing aircraft, nonconventional applications and atmospheric effects, the development of new flight-test procedures for system identification purposes, and specific aircraft model enhancements and corresponding parameter estimation. Comprehensive tools were developed that support this research and can be applied to a variety of different problems and types of vehicles. The paper starts with a short description of the different system identification methods used at DLR and the corresponding tools. The discussion of flight-test procedures and maneuver design as well as sensor fusion and flight-path reconstruction provides information on how to optimize the flight tests for system identification and to arrive at a consistent flight-test database. The examples for fixed-wing aircraft provide information on identification including abnormal conditions such as icing and interaction with atmospheric disturbances as well as modeling of structural mechanics and loads. The identification of high-order rotorcraft models that account for rotor and engine dynamics and even structural modes is discussed, and the identification of rotor mast moments as well as the identification of non-physics-based models and their integration into physics-based models are also covered. A final section shows that system identification can also be used to derive models for gyroplanes and parachutes as well as to derive control equivalent turbulence input models and to estimate complex wind field geometries. Thus, a broad overview of possible applications of system identification is given.