GLOMAP Approach for Nonlinear System Identification of Aircraft Dynamics Using Flight Data Monika Marwaha * John Valasek † and Puneet Singla ‡ 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. Introduction The modeling and identification of nonlinear aircraft dynamics through use of measured experimental data is a problem of considerable importance. To derive control laws, a mathematical model of the system dynamics between control input and output states is desired. Also real time estimation of the input-output model of the system is required. Many well developed and efficient identification algorithms exists for linear system identification. These are often employed to model nonlinear systems when nonlinearities are small, or the system operates in a small locally linear regime. Techniques such as Modified Maximum Likelihood * Graduate Research Assistant,Aerospace Engineering Department, Texas A&M University, 3141 TAMU, College Station, TX 77843- 3141 † Associate Professor and Director, Vehicle Systems & Control Laboratory, Aerospace Engineering Department, Texas A&M University, 3141 TAMU, College Station, TX 77843-3141, Associate Fellow AIAA, valasek@tamu.edu, http://jungfrau.tamu.edu/valasek ‡ Assistant Professor, Department of Mechanical & Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260 1 of 19 American Institute of Aeronautics and Astronautics AIAA Atmospheric Flight Mechanics Conference and Exhibit 18 - 21 August 2008, Honolulu, Hawaii AIAA 2008-6895 Copyright © 2008 by Monika Marwaha and John Valasek and Puneet Singla. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.