Direct prediction-error identification of unstable nonlinear systems applied to flight test data

Control system design for advanced, highly agile fighter aircraft, with unstable nonlinear aerodynamic characteristics, rely heavily on flight mechanical simulations. This makes the accuracy of the aerodynamic model in the simulators very important. Here, two methods for estimating parameters of nonlinear unstable systems where the control system is unknown are presented. Both approaches are direct prediction-error methods, either with a directly parametrized observer or with an Extended Kalman Filter as a predictor. These methods have been validated on simulated data, as well as on real flight test data and all approaches show promising results.