2020 | OriginalPaper | Chapter
Estimation of States and Parameters with Dual Extended Kalman Filters for Active Roll Control
Authors: Seongjin Yim, Jaewon Nah, Manbok Park
Publisher: Springer International Publishing
Published in:
Advances in Dynamics of Vehicles on Roads and Tracks
Abstract
This paper presents a method that designs dual extended Kalman filters (EKFs) for active roll control. There are two Kalman filters: state and parameter estimators. The roll angle of a vehicle is estimated by the state estimator with roll rate measurement. It has been well known that the recursive least square (RLS) or parameter estimation scheme can be represented with Kalman filter. Using the fact, the parameters of a vehicle model are estimated by the parameter estimator using the estimated roll angle. Simulation and experiments are done to validate the proposed dual EKFs for active roll control.
