This paper is mainly devoted to estimating the lateral velocity of vehicle with an observer synthesis method using a cost-effective 6D inertial measurement unit. The four sources of observer to be synthesized come from the dynamics model, the kinematics model, and a LuGre tire model, respectively. The weight of each source is calculated by considering the nonlinear extent of each tire by means of the adaptation of tire cornering stiffness, which is adapted and updated using the dynamics model observer with the information obtained from the kinematics model observer. Then using the weighted sum of the four sources as the reference information for a modified kinematics observer, the final estimation can provide smooth and accurate results that are robust to sensor drifts and measurement noise, which is verified with several dSPACE based hardware-in-the-loop simulations.
FengJChenSQiZ.Coordinated chassis control of 4WD vehicles utilizing differential braking, traction distribution and active front steering. IEEE Access2020; 8: 81055-81068.
2.
ZhaoJWongPKMaX, et al. Chassis integrated control for active suspension, active front steering and direct yaw moment systems using hierarchical strategy. Veh Syst Dyn2017; 55(1): 72–103.
3.
MengQZhaoTQianC, et al. Integrated stability control of AFS and DYC for electric vehicle based on non-smooth control. Int J Syst Sci2018; 49(7): 1518–1528.
4.
SelmanajDCornoMPanzaniG, et al. Robust vehicle sideslip estimation based on kinematic considerations. IFAC Pap Online2017; 50(1): 14855–14860.
5.
FarrellyJWellsteadP (eds). Estimation of vehicle lateral velocity. In: IEEE international conference on control applications, Dearborn, MI, 15 September–18 November 1996.
6.
DakhlallahJGlaserSMammarSSebsadjiY (eds). Tire-road forces estimation using extended Kalman Filter and sideslip angle evaluation. In: American control conference, Seattle, WA, 11–13 June 2008.
7.
KatriniokAAbelD.Adaptive EKF-based vehicle state estimation with online assessment of local observability. IEEE Trans Control Syst Technol2016; 24(4): 1368–1381.
8.
ChenTXuXChenL, et al. Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles. Mech Syst Signal Process2018; 101: 377–388.
9.
Brennan, SeanN, Brown, et al. Lateral vehicle state and environment estimation using temporally previewed mapped lane features. IEEE trans Intell Transp Syst2015; 16(3): 1601–1608.
10.
Anh-TuNGuerraT-MSentouhC.Simultaneous estimation of vehicle lateral dynamics and driver torque using LPV unknown input observer. IFAC Pap Online2018; 51(26): 13–18.
11.
YouSHHahnJOLeeH.New adaptive approaches to real-time estimation of vehicle sideslip angle. Control Eng Pract2009; 17(12): 1367–1379.
12.
GripHFImslandLJohansenTA, et al. Vehicle sideslip estimation. IEEE Control Syst Mag2009; 29(5): 36–52.
13.
AokiYInoueTHoriY (eds). Robust design of gain matrix of body slip angle observer for electric vehicles and its experimental demonstration. In: 8th IEEE International Workshop on Advanced Motion Control, 2004, Kawasaki, Japan, 28 March 2004.
14.
LiaoYWBorrelliF.An adaptive approach to real-time estimation of vehicle sideslip, road bank angles and sensor bias. IEEE Trans Veh Technol2019; 68(8): 7443–7454.
15.
LeeH.Reliability indexed sensor fusion and its application to vehicle velocity estimation. J Dyn Syst Meas Control2006; 128(2): 236.
16.
OhJJChoiSB.Vehicle velocity observer design using 6-D IMU and multiple-observer approach. IEEE Trans Intell Transp Syst2012; 13(4): 1865–1879.
17.
AkhenakAChadliMMaquinD, et al. State estimation via multiple observer with unknown inputs: application to the three tank system. In: 5th IFAC symposium on fault detection, supervision and safety for technical processes, safeprocess’2003, Washington, June 2003.
18.
RajamaniR.Vehicle dynamics and control. J Jpn Fluid Power Syst Soc2012; 39(8): 111.
19.
TibshiraniR.Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol1996; 58(1): 267–288.
20.
EksiogluEMTancAK.RLS algorithm with convex regularization. IEEE Signal Process Lett2011; 18(8): 470–473.
21.
NortonJP.System identification and control design using P.I.M. + software: I. D. Landau. Automatica1991; 1068–1069.
22.
RehmA (ed.). Estimation of vehicle roll angle. In: 2010 4th international symposium on communications, control and signal processing (ISCCSP), Limassol, Cyprus, 3–5 March 2010.
23.
Canudas de WitCTsiotrasPVelenisE, et al. Dynamic tire friction models for vehicle traction control. Veh Syst Dyn2003; 39: 189–226.
24.
Canudas de WitCTsiotrasP. Dynamic tire friction models for vehicle traction control. In: Proceedings of the 38th IEEE conference on decision and control, Phoenix, AZ, 7–10 December 1999.
25.
KlompMGaoYBruzeliusF.Longitudinal velocity and road slope estimation in hybrid electric vehicles employing early detection of excessive wheel slip. Veh Syst Dyn2014; 52(Suppl. 1): 172–188.
26.
RezaeianAKhajepourAMelekW, et al. Simultaneous vehicle real-time longitudinal and lateral velocity estimation. IEEE Trans Veh Technol2017; 66: 1950–1962.
