Abstrak - Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 1 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 2 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 3 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 4 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 5 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
BAB 6 Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
COVER Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
DAFTAR PUSTAKA Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
LAMPIRAN Andhika Prayoga Tama
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
This study presents a real-time aircraft parameter estimation framework using
the Recursive Least Squares (RLS) algorithm with a forgetting factor, validated
through high-fidelity simulations in FMAE flight simulator. Real-time estimation of
aerodynamic parameters is crucial for flight control system development and
accurate aircraft modeling. The RLS algorithm enables continuous adaptation to
changes in flight dynamics and measurement noise, outperforming traditional
methods such as Ordinary Least Squares (OLS), which lack adaptiveness.
Comparative simulation results show that RLS with a forgetting factor yields more
accurate and responsive parameter estimates under various dynamic flight
conditions. The integration with X-Plane allows for realistic, scenario-rich testing,
making the method suitable for real-time system identification applications. These
results highlight the potential of RLS-based estimation in advancing robust and
accurate flight dynamic modeling.
Perpustakaan Digital ITB