Nonlinear model identification of airplane based on flight data using state estimation

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Dr. Lantos Béla
Department of Control Engineering and Information Technology

Flight control is one of the leading fields of modern control theory. Its main driving forces are military application, however civil public transport and freight transport require precise control. Like all systems that need to be controlled, such as mechatronic system, production or biological process, it is essential to know the structure and model parameters of the system. In this thesis procedures for measurement data processing are described. Identification aiding estimation methods were developed for standing, stationary and flying phase of the aircraft. Data series measured on an Ultrastick UAV (unmanned aerial vehicle) was used to demonstrate these estimation methods. Parts of the non-dimensional parameters of the Ultrastick aircraft were identified. The non-public SIDPAC MATLAB toolbox was used for the identification process, which is an appendix for book the Aircraft System Identification Theory and Practice.

In Chapter 1 a survey of flight technique ideas is given. Chapter 2 deals with regression and state estimation fundamentals including EKF. Chapter 3 presents signal filtering methods and their usage for real data. Chapter 4 deals with deterministic state estimation of an airplane. In this chapter the state estimation execution results are presented. In Chapter 5 parameters of the Ultrastick UAV are described and the effective influences of the state estimation method for the parameter identification are shown. The identification of the Cy and Cn non-dimensional aerodynamic parameters

were presented. In Chapter 6 a genetic algorithm based idea for determining the unknown vector connecting the center of gravity and the center of sensor is sketched. In Chapter 7 the components of the developed software are described and some of the future development directions are presented.


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