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Vol: 60(74) No: 1 / March 2015 Simultaneous Aircraft Nonlinear Model and Inertial Parameter Identification Based on Real Flight Data Loránd Lukács Department of Control engineering and Information Technology, Budapest University of Technology and Economics, Magyar Tudósok krt.2, H-1117 Budapest, Hungary, e-mail: lukacsl@iit.bme.hu Béla Lantos Department of Control engineering and Information Technology, Budapest University of Technology and Economics, Magyar Tudósok krt.2, H-1117 Budapest, Hungary, e-mail: lantos@iit.bme.hu Keywords: aircraft nonlinear dynamic model, aircraft identification, inertial parameter identification, center of gravity, inertia matrix identification. Abstract The scope of the paper is the identification of an aircraft’s nonlinear dynamic model together with its inertial parameters. The platform for identification is a sailplane with the presented method using real flight data. It is assumed that the aircraft has neither an inbuilt navigational system, nor any sensors for recording input signals. Therefore, the aircraft states are estimated using externally mounted GPS, IMU and magnetometer readings and a multi-mode, multilevel state estimator based on EKF. The input signals consisting of aileron, elevator and rudder surface deflections are determined through image processing. The state estimation and input signal determination is detailed in previous papers while the current paper concentrates on the identification of the center of gravity and inertia matrix, together with the force-torque model. The force-torque model is based on the rigid body dynamic equations containing additional weighted nonlinear terms for the 3D forces and torques. The dominating nonlinear functions are selected according to physical considerations with their parameters determined using an SVD technique. The disturbance caused by wind effects are also taken into consideration. The inertial parameters are determined using global search based optimization on an appropriately selected objective function. References [1] V. Klein and E. Morelli, Aircraft System Identification, Theory and Practice. American Institute of Aeronautics and Astronautics, 2006. [2] J. Farrel, GPS with High Rate Sensors. McGraw-Hill, 2008. [3] S. Myschik and G. Sachs, “Flight testing an integrated wind/airdata and navigation system for general aviation aircraft,” in Proc. AIAA Guidance, Navigation and Control Conference, Hilton Head, USA, 2007, pp. 1–21. [4] A. Dorobantu, A. Murch, B. Mettler, and G. Balas, “System identification for small, low-cost, fixed-wing unmanned aircraft,” Journal of Aircraft, vol. 50, no. 4, pp. 1117–1130, 2013. [5] L. Lukács and B. Lantos, “Identification of the nonlinear dynamic model of sailplanes involving state estimation and image processing for actuator signal computation,” in Proc. 12th IEEE International Symposium on Intelligent Systems and Informatics SISY2014, Subotica, Serbia, 2014, pp. 227–232. [6] L. Lukács and B. Lantos, “Data fusion and primary image processing for aircraft identification,” Periodica Polytechnica Electrical Engineering and Computer Science, vol. 56, no. 3, pp. 83–94, 2012. [7] R. Hartley and A. Zissermann, Multiple View Geometry in Computer Vision. Cambridge University Press, 2003. [8] B. Lantos and L. Márton, Nonlinear Control of Vehicles and Robots. Springer, London, 2011. [9] L. Lukács and B. Lantos, “GPS and IMU based state estimation method for aircraft INS navigation,” Scientific Bulletin of Politehnica University of Timisoara, Transactions on Automatic Control and Computer Science, vol. 9, no. 1, pp. 9–17, 2014. [10] L. Lukács and B. Lantos, “Nonlinear dynamic model identification of airplanes including inertial parameters and force-torque relations based on real flight data,” in Proc. 10th IEEE Jubilee International Symposium on Applied Computational Intelligence and Informatics SACI 2015, Timisoara, Romania, 2015, pp. 393–398. [11] R.-E. Precup and S. Preitl, “Popov-type stability analysis method for fuzzy control systems,” in Proc. Fifth European Congress on Intelligent Technologies and Soft Computing (EUFIT’97), Aachen, Germany, 1997, vol. 2, pp. 1306–1310. |