TY - JOUR
T1 - State estimation in roll dynamics for commercial vehicles
AU - Ma, Zeyu
AU - Ji, Xuewu
AU - Zhang, Yunqing
AU - Yang, James
N1 - Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/3/4
Y1 - 2017/3/4
N2 - Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.
AB - Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.
KW - Sliding-mode observer
KW - and commercial vehicles
KW - state estimation
KW - vehicle roll dynamics
UR - http://www.scopus.com/inward/record.url?scp=85002397942&partnerID=8YFLogxK
U2 - 10.1080/00423114.2016.1262049
DO - 10.1080/00423114.2016.1262049
M3 - Article
AN - SCOPUS:85002397942
SN - 0042-3114
VL - 55
SP - 313
EP - 337
JO - Vehicle System Dynamics
JF - Vehicle System Dynamics
IS - 3
ER -