Akademik Veri Yönetim Sistemi
23th International Istanbul Scientific Research Congress on Life, Engineering, Architecture, and Mathematical Sciences, İstanbul, Türkiye, 20 - 22 Kasım 2025, ss.1165-1174, (Tam Metin Bildiri)
Vehicle dynamics modeling is commonly used to analyze roll, pitch, and yaw angles resulting from
vehicle maneuvering, road surfaces, and road profiles in order to improve a vehicle's road holding.
Researchers begin with a simpler model, a two-degree-of-freedom (2DoF) vehicle model, for control
system design without parameters such as vertical forces, brake pressure, and wind effects acting on a
vehicle. Studies have shown that designing the control system by making predictions on the model will
improve its performance in controlling the system with less computational load. The best results for
estimation are obtained using the Recursive Least Squares (RLS) algorithm and the Kalman Filter. The
RLS algorithm is used for parameter estimation, such as vehicle tire parameters, while the Kalman Filter
is used for state estimation, such as lateral velocity and yaw rate, yielding more accurate results. In this
study, using a 2DoF vehicle model, the performance of the RLS algorithm and Kalman Filter in
estimating lateral and yaw rates during sudden maneuvers on dry, wet, and icy road surfaces was
observed at a basic level. The results show that when these two methods are used in an integrated manner
in parameter estimation and lateral velocity estimation, satisfactory results are obtained.