Robust adaptive flight-path reconstruction technique for nonsteady longitudinal flight test maneuvers

Abstract

Computational schemes are presented that allow accurate reconstruction of the flight path of the longitudinal aircraft motion in real time. The reconstruction of the flight path is formulated as a discrete, linear, time-variant state reconstruction problem that can be solved via Kalman filtering techniques. This imposes some conditions upon the flight test equipment. A reliable square-root covariance filter implementation is chosen and further developed in a fully adaptive flight-path reconstruction scheme. Therefore, the chosen Kalman filter implementation is modified to cope with several practical problems such as the automatic control of the convergence of the Kalman filter recursions, time-varying bias errors on the input signal of the system model used in the Kalman filter, variations in the wind, and the changing aircraft dynamics owing to a change in nominal flight condition. The developed solutions for these problems are all implemented in a numerically stable way, which guarantees the overall flight-path reconstruction scheme to be robust. Furthermore, some special features of the system model are exploited to make the algorithmic implementation very efficient. These different capabilities are highlighted in an experimental analysis using simulated flight test data.