Flight test results of Observer/Kalman Filter Identification of the Pegasus unmanned vehicle

摘要

Flight testing is the preferred means of obtaining accurate, locally linear, dynamic models of nonlinear aircraft dynamics. In this paper, decoupled longitudinal and lateral/directional linear dynamic models of an unmanned air vehicle are identified using the Observer/Kalman Filter Identification method. This method is a time-domain technique that identifies a discrete input-output mapping from known input and output data samples. The method is developed for flight testing, including details of instrumentation, measurements, and data post-processing techniques such as nonlinear estimation. Multiple flight tests were conducted, and experimental examples for longitudinal and lateral/directional dynamics are presented, including the model selection process. Fidelity of the identified linear models to the nonlinear plant is validated by comparing measured and model predicted outputs with measured inputs from flight test. Mean squared errors and the Theil information coefficient are used as accuracy metrics. Results presented in the paper demonstrate that the linear models reproduced from flight test results are acceptable representations of the nonlinear aircraft dynamics in the cruise configuration.