Nonlinear model identification of airplanes using noisy flight data

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Dr. Lantos Béla
Department of Control Engineering and Information Technology

Aircraft control is one of the most progressive fields of the modern control

techniques. Control methods implemented on embedded flight computers enhance not

only robot pilot functions but also support the pilot. These are known as stability and

control augmentation systems. There are also several civil applications of these methods;

for instance, in case of vehicles of commercial and personal air transport. However, the

military is the most motivated to improve these applications and methods. It is

indispensable to have an accurate nonlinear dynamic model of the aircraft to achieve an

adequate control that satisfies all of the requirements. A mathematical model of the

aircraft can be derived using kinematic, dynamic and navigational equations of the rigid

body. Contrarily, it is a more difficult process to determine its parameters.

The determination of the nonlinear model and its parameters are parts of the field of

system identification. In this first part of my MSc thesis I reviewed several identification

methods such as the Maximum Likelihood (ML) method both in the time domain and in

the frequency domain. Particular focus is placed on the output error method. I present an

outline of filtering and smoothing the signals playing important roles in the model

parameter determination. As a last part of this work I demonstrate the methods

mentioned above applying the SIDPAC Toolbox developed by NASA, and I delineate

some ideas and plans to continue this work.


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