Journal
VEHICLE SYSTEM DYNAMICS
Volume 60, Issue 2, Pages 699-717Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00423114.2020.1838565
Keywords
Roll centre estimation; adaptive sliding mode observer (ASMO); extended Kalman filter (EKF); vehicle roll dynamics; vehicle transient dynamics
Categories
Funding
- National Natural Science Foundation of China [51675217]
- Young Elite Scientists Sponsorship Program by CAST [2016QNRC001]
- China Automobile Industry Innovation and Development Joint Fund [U1564213]
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This paper presents online roll center (RC) estimation methodologies, including adaptive sliding mode observer (ASMO) and extended Kalman filter (EKF), considering vehicle transient dynamics. Simulation results demonstrate the successful estimation of RC using ASMO and EKF for online vehicle RC estimation.
The roll centre (RC) is a core parameter for vehicle lateral dynamics and control, which can be obtained via suspension geometry configuration or suspension kinematics and compliance (K&C) test. However, these methodologies are used for laboratory tests and are suitable at low lateral acceleration. In other words, the RC is hard to measure directly while the vehicle is running on the road. In this paper, the online RC estimation methodologies including the adaptive sliding mode observer (ASMO) and the extended Kalman filter (EKF) only with roll rate are proposed considering vehicle transient dynamics. The performance of these algorithms is evaluated and compared with the recursive least square with disturbance observer algorithm (RLSDA) via vehicle dynamics study. Simulation results manifest that, compared with the RLSDA with three roll signals, the proposed ASMO and EKF, only with roll rate, can estimate RC successfully for both the transient and steady-state cases and can be applied for online vehicle RC estimation. In detail, the proposed ASMO is recommended for the steady-state case, and the proposed EKF is recommended for the transient case. Furthermore, the static RC is recommended as the estimation initial value to improve estimation.
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