GLOMAP Approach for Nonlinear System Identification of Aircraft Dynamics Using Flight Data

This paper introduces the Global-Local Mapping Approximation algorithm as a can-didate for identifying nonlinear, six degree-of-freedom rigid body aircraft dynamics. The technique models the nonlinear dynamical model as a sum of linear model and nonlinear model. The linear model dynamics are assumed to be perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the linear model. Lya-punov stability analysis is used to derive the learning laws. To demonstrate the suitability of the algorithm for nonlinear system identification of aircraft dynamics, a longitudinal and a lateral/directional example using nonlinear simulation data, and flight test data are conducted. The true nonlinear model is generated using both the six degree-of-freedom nonlinear equations of motion of an aircraft, and by flight test data. Results presented in the paper demonstrate the utility of the Global-Local Mapping Approximation for the re-alistic cases of an unknown control distribution matrix B and unknown influence coefficient matrix C. I