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Abstrak - Yeremia Siagian
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Aircraft system identification is a crucial factor influencing the development
of aircraft performance. The process of system identification involves estimation
and modeling of various systems within an aircraft. Optimization
methods play a pivotal role in enhancing the accuracy and efficiency of parameter
estimation, addressing challenges such as non linearity, noise, and
system complexity. The Output Error Method (OEM) is a widely used technique
in aircraft system identification that can deal with measurement noise.
The aim of the research is to explore and explain an alternative optimization
methods in Output Error Method (OEM) framework for aircraft system
identification and compare its performance with existing methods. The Gauss-
Newton and Levenberg-Marquardt algorithms are widely used for estimating
parameters in nonlinear models, with the former optimizing through successive
linear approximations and the latter combining gradient descent and Gauss-
Newton to enhance convergence stability. In this work, the Nelder-Mead and
Broyden-Fletcher-Goldfarb-Shanno (BFGS) are compared with Gauss-Newton
and Levenberg-Marquardt method as a baseline algorithm that works good
within the OEM framework. The data utilized for evaluation is from book
of R.V. Jategaonkar’s, specifically from a lateral-directional motion test case
conducted on the ATTAS aircraft. Results indicate that Gauss-Newton and
Levenberg-Marquardt method still produce best parameter estimates compared
to the alternative methods.