Obtaining precise yaw rate and lateral velocity as well as developing a nonlinear controller becomes more and more essential for improving the vehicle handling performance. Different from traditional methods, a data-driven subspace-based prediction approach is introduced by integrating propagator with predictor-based subspace identification method in this paper. Based on an identifiable vehicle model, the prediction process is validated by standard road tests data. To employ this data-driven prediction method in the vehicle handling stabilization and solve the controlling problem of nonlinear lateral dynamic system, a feedback linearization controller based on the new piecewise tire model is elaborately developed. On account of that the one-step prediction output reduces the time delay between actuator and lateral dynamic response, the subspace-based controller can theoretically improve the vehicle handling performance. By road simulation results, the proposed feedback linearization controller combined with a data-driven subspace-based prediction method greatly enhances the handling performance and provides a more effective technique for both vehicle parameter estimation and handling stabilization.
van ZantenAT. Bosch ESP systems: 5 years of experience, Warrendale, PA: SAE International, 2000.
2.
MammarSKoenigD. Vehicle handling improvement by active steering. Veh Syst Dyn2002; 38: 211–242.
3.
TjonnasJJohansenTA. Stabilization of automotive vehicles using active steering and adaptive brake control allocation. IEEE Trans Control Syst Technol2010; 18: 545–558.
4.
Oraby WA. Improvement of vehicle lateral stability during overtaking process by active front steering system. SAE Paper 2007-01-0809, 2007.
5.
SongJ. Design and comparison of AFS controllers with PID, fuzzy-logic, and sliding-mode controllers. Adv Mech Eng2013; 5: 1–13.
6.
ShimTChangSLeeS. Investigation of sliding-surface design on the performance of sliding mode controller in antilock braking systems. IEEE Trans Veh Technol2008; 57: 747–759.
7.
MirzaeiAMoallemMDehkordiBMet al.Design of an optimal fuzzy controller for antilock braking systems. IEEE Trans Veh Technol2006; 55: 1725–1730.
8.
LiBYuF. Design of a vehicle lateral stability control system via a fuzzy logic control approach. Proc IMechE, Part D: J Automobile Engineering2010; 224: 313–326.
9.
RajamaniR. Vehicle dynamics and control, New York: Springer, 2011.
10.
AbeM. Vehicle handling dynamics: Theory and application, London: Elsevier, 2009.
11.
GengCMostefaiLDenaïMet al.Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer. IEEE Trans Ind Electron2009; 56: 1411–1419.
12.
ZhuHLiLJinMet al.Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control. Proc IMechE, Part D: J Automobile Engineering2013; 227: 1431–1445.
13.
RezaiMShiraziKH. Robust handling enhancement on a slippery road using quantitative feedback theory. Proc IMechE, Part D: J Automobile Engineering2014; 228: 426–442.
14.
Ouladsine M, Shraim H, Fridman L, et al. Vehicle parameter estimation and stability enhancement using the principles of sliding mode. In: Proceedings of American control conference, New York, NY, USA, 9–13 July 2007, pp.5224–5229.
15.
BestMCGordonTDixonP. An extended adaptive Kalman filter for real-time state estimation of vehicle handling dynamics. Veh Syst Dyn2000; 34: 57–75.
16.
GadolaMChindamoDRomanoMet al.Development and validation of a Kalman filter-based model for vehicle slip angle estimation. Veh Syst Dyn2014; 52: 68–84.
HoutzagerIvan WingerdenJ-WVerhaegenM. Recursive predictor-based subspace identification with application to the real-time closed-loop tracking of flutter. IEEE Trans Control Syst Technol2012; 20: 934–949.
21.
GuanXYuanMZhangJ. Application of subspace-based method in vehicle handling dynamic model identification and properties estimation. Int J Veh Des2011; 56: 125–145.
22.
van der Veen G, van Wingerden J and Verhaegen M. Closed-loop MOESP subspace model identification with parametrisable disturbances. In: 2010 49th IEEE conference on decision and control (CDC), 2010, pp.2813–2188. New York: IEEE.
23.
van der VeenGVan WingerdenJ-WBergamascoMet al.Closed-loop subspace identification methods: An overview. IET Control Theory Appl2013; 7: 1339–1358.
24.
ChiusoA. The role of vector autoregressive modeling in predictor-based subspace identification. Automatica2007; 43: 1034–1048.
25.
ChiusoA. On the relation between CCA and predictor-based subspace identification. IEEE Trans Autom Control2007; 52: 1795–1812.
26.
GustafssonT. Instrumental variable subspace tracking using projection approximation. IEEE Trans Signal Process1998; 46: 669–681.
27.
GuanXQBaTYZhangJW. Vehicle handling dynamics modelling by a data-driven identification method. Veh Syst Dyn2015; 53: 1580–1598.
28.
BaTYGuanXQZhangJWet al.Application of recursive subspace method in vehicle lateral dynamics model identification. Math Probl Eng2016.
29.
MercèreGBakoLLecœucheS. Propagator-based methods for recursive subspace model identification. Signal Process2008; 88: 468–491.
30.
MiguelFLFMiguelFLFThomasKC. Theoretical and experimental modal analysis of a cantilever steel beam with a tip mass. Proc IMechE, Part C: J Mechanical Engineering Science2009; 223: 1535–1541.
31.
LiawD-CChungW-C. A feedback linearization design for the control of vehicle’s lateral dynamics. Nonlin Dyn2008; 52: 313–329.
32.
KrenerAJFeedback linearization. In: JBaillieulJCWillems (eds). Mathematical control theory, New York: Springer, 1999, pp. 66–98.
HsuY-HJGerdesJC. Stabilization of a steer-by-wire vehicle at the limits of handling using feedback linearization, Orlando, FL: ASME, 2005, pp. 483–492.
35.
BoadaBLBoadaMJLDiazV. Fuzzy-logic applied to yaw moment control for vehicle stability. Veh Syst Dyn2005; 43: 753–770.
36.
EsmailzadehEGoodarziAVossoughiGR. Optimal yaw moment control law for improved vehicle handling. Mechatronics2003; 13: 659–675.
