TY - JOUR
T1 - Vehicle sideslip estimator using load sensing bearings
AU - Madhusudhanan, Anil Kunnappillil
AU - Corno, Matteo
AU - Holweg, Edward
PY - 2016
Y1 - 2016
N2 - This paper investigates the potential of load based vehicle sideslip estimation. Different techniques to measure tyre forces have been presented over the years; so far no technique has made it to the market. This paper considers a new technology based on load sensing bearings, which provides tyre force measurements. Based on the features of the sensor, a vehicle sideslip angle estimator is designed, analyzed and tested. The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics. Extensive simulation tests along with a detailed analysis of experimental tests performed on an instrumented vehicle prove that the load based estimation outperforms the kinematic model-based benchmark yielding a root mean square error of 0.15°.
AB - This paper investigates the potential of load based vehicle sideslip estimation. Different techniques to measure tyre forces have been presented over the years; so far no technique has made it to the market. This paper considers a new technology based on load sensing bearings, which provides tyre force measurements. Based on the features of the sensor, a vehicle sideslip angle estimator is designed, analyzed and tested. The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics. Extensive simulation tests along with a detailed analysis of experimental tests performed on an instrumented vehicle prove that the load based estimation outperforms the kinematic model-based benchmark yielding a root mean square error of 0.15°.
KW - Kalman filter
KW - Load sensing bearing
KW - Observability
KW - State estimation
KW - Vehicle sideslip
UR - http://www.scopus.com/inward/record.url?scp=84971475784&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2016.05.008
DO - 10.1016/j.conengprac.2016.05.008
M3 - Article
AN - SCOPUS:84971475784
SN - 0967-0661
VL - 54
SP - 46
EP - 57
JO - Control Engineering Practice
JF - Control Engineering Practice
ER -