System Identification of Flight Mechanical Characteristics

Detta är en avhandling från Linköping : Linköping University Electronic Press

Sammanfattning: With the demand for more advanced fighter aircraft, relying on relaxed stability or even unstable flight mechanical characteristics to gain flight performance, more focus has been put on model-based system engineering to help with the design work. The flight control system design is one important part that relies on this modeling. Therefore it has become more important to develop flight mechanical models that are highly accurate in the whole flight envelop. For today’s newly developed fighters, the basic aircraft characteristics change between linear and nonlinear as well as stable and unstable as an effect of the desired capability of advanced maneuvering at subsonic, transonic and supersonic speeds.This thesis combines the subject of system identification, which is the art of building mathematical models of dynamical systems based on measurements, with aeronautics in order to find methods to identify flight mechanical characteristics from flight tests. Here, a challenging aeronautical identification problem combining instability and nonlinearity is treated.Two aspects are considered. The first is identification during a flight test with the intent to ensure that enough information is available in the resulting test data. Here, a frequency domain method is used. This idea has been taken from an existing method to which some improvements have been made. One of these improvements is to use an Instrumental Variable approach to take care of disturbances coming from atmospheric turbulence. The method treats linear systems that can be both stable and unstable. The improved method shows promising results, but needs further work to become robust against outliers and missing data.The other aspect is post-flight identification. Here, five different direct identification methods, which treat unstable and nonlinear systems, have been compared. Three of the methods are variations of the prediction-error method. The fourth is a parameter and state estimation method and the fifth method is a state estimation method based on an augmented system approach. The simplest of the prediction-error methods, based on a parametrized observer approach, is least sensitive to noise and initial offsets of the model parameters for the studied cases. This approach is attractive since it does not have any parameters that the user has to tune in order to get the best performance.All methods in this thesis have been validated on simulated data where the system is known, and have also been tested on real flight test data.

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