Observer/Kalman filter identification for online system identification of aircraft
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abstract
The observer/Kalman filter identification method is applied to the problem of online system identification of accurate, locally linear, aircraft dynamic models of nonlinear aircraft. It is a time-domain technique that identifies a discrete input-output mapping from known input and output data samples, without user imposed a priori assumptions about model structure or model order. The basic formulation of observer/Kalman filter identification specific to the aircraft problem is developed and implemented in a nonlinear, six-degree-of-freedom simulation of an AV-8B Harrier. A similar simulation of a generic uninhabited combat aerial vehicle is also used. Numerical examples are presented, consisting of longitudinal and lateral/directional successive online identifications at different nonperfect trim conditions, identification with sensor noise on multiple channels, and identification with discrete gusts. Accuracy of the identified linear system models to the nonlinear plant is quantified with comparison of eigenvalues, the Vinnicombe gap metric, and time history matching. Results demonstrate that the observer/Kalman filter identification method is suitable for aircraft online identification of locally linear aircraft models and is generally insensitive to moderate intensity Gaussian white sensor noise and for light to moderate intensity discrete gusts.
