A model-based vehicle stability controller is proposed in this paper. Since a model-based controller highly depends on the model accuracy, the lateral forces of the tyres and the instantaneous cornering stiffnesses of the predictive vehicle model are estimated online in order to deal with the model mismatch. The model-based controller calculates a desired yaw moment according to the prevailing errors in the the yaw rate and the side-slip angles of the vehicle. Then, by using linearization, an optimal braking force allocation algorithm is proposed to allocate the yaw moments to the target slip ratios of each wheel. Simulations are carried out with a full-vehicle model in CarSim software. The effectiveness and the robustness of the proposed control algorithm are evaluated through simulation cases of open-loop and closed-loop manoeuvres.
ManningWJCrollaDA. A review of yaw rate and sideslip controllers for passenger vehicles. Trans Inst Measmt Control2007; 29(2): 117–135.
2.
Van ZantenAT. Evolution of electronic control systems for improving the vehicle dynamic behavior. In: 6th international symposium on advanced vehicle control, Hiroshima, Japan, 9–13 September 2002, pp. 7–15. Tokyo: JSAE.
3.
YuFLiD-FCrollaDA. Integrated vehicle dynamics control – state-of-the art review. In: IEEE vehicle power and propulsion conference, Harbin, People’s Republic of China, 3–5 September 2008, pp. 1–6. New York: IEEE.
4.
GordonTHowellMBrandaoF. Integrated control methodologies for road vehicles. Veh System Dynamics2003; 40(1–3): 157–190.
5.
TsengHEAshrafiBMadauD. The development of vehicle stability control at Ford. IEEE/ASME Trans Mechatronics1999; 4(3): 223–234.
6.
Van ZantenAT. Bosch ESP systems: 5 years of experience. SAE paper 2000-01-1633, 2000.
7.
FalconePBorrelliFAsgariJ. Predictive active steering control for autonomous vehicle systems. IEEE Trans Control Systems Technol2007; 15(3): 566–580.
8.
BarbarisiOPalmieriGScalaS. LTV-MPC for yaw rate control and side slip control with dynamically constrained differential braking. Eur J Control2009; 15(3): 468–479.
9.
ChangSGordonTJ. Improvement of vehicle dynamics using model-based predictive control. SAE paper 2009-01-0427, 2009.
10.
CanaleMFagianoLSignorileMC. A model predictive control approach to vehicle yaw control using identified models. Proc IMechE Part D: J Automobile Engineering2012; 226(5): 577–590.
11.
PalmieriGBarbarisiOScalaS. An integrated LTV-MPC lateral vehicle dynamics control: simulation results. In: Automotive model predictive control, Lecture Notes in Control and Information Science, Vol 402. Berlin: Springer, 2010, pp. 231–255.
12.
BaTGuanXZhangJ. Vehicle predictive control based on the recursive subspace identification method. Proc IMechE Part D: J Automobile Engineering2014: 229(8): 1094–1109.
13.
XiongLYuZWangY. Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation. Veh System Dynamics2012; 50(6): 831–846.
14.
FujimotoHTakahashiNTsumasakaA. Motion control of electric vehicle based on cornering stiffness estimation with yaw-moment observer. In: 9th IEEE international workshop on advanced motion control, Istanbul, Turkey, 27–29 March 2006, pp. 206–211. New York: IEEE.
15.
TøndelPJohansenT. Control allocation for yaw stabilization in automotive vehicles using multiparametric nonlinear programming. In: 2005 American control conference, Portland, Oregon, USA, 8–10 June 2005, pp. 453–458. New York: IEEE.
16.
IkedaYSuzukiRChidaY. Driving/braking force distribution of four wheel vehicle by quadratic programming with constraints. J System Des Dynamics2012; 6: 417–430.
17.
MokhiamarOAbeM. How the four wheels should share forces in an optimum cooperative chassis control. Control Engng Practice2006; 14(3): 295–304.
18.
DuHLamJCheungK-C. Side-slip angle estimation and stability control for a vehicle with a non-linear tyre model and a varying speed. Proc IMechE Part D: J Automobile Engineering2014; 229(4): 486–505.
19.
DavoodabadiIRamezaniAMahmoodi-kM. Identification of tire forces using dual unscented Kalman filter algorithm. Nonlinear Dynamics2014; 78(3): 1907–1919.
20.
SierraCTsengEJainA. Cornering stiffness estimation based on vehicle lateral dynamics. Veh System Dynamics2006; 44(Suppl 1): 24–38.
21.
WelchGBishopG. An introduction to the Kalman filter. In: SIGGRAPH2001, Los Angeles, California, USA, 12–17 August 2001, Course 8, pp. 1–48. New York: ACM.
22.
SienelW. Estimation of the tire cornering stiffness and its application to active car steering. In: 36th IEEE conference on decision and control, San Diego, California, USA, 10–12 December 1997, pp. 4744–4749. New York: IEEE.
23.
HaffnerLKozekMShiJ. Estimation of the maximum friction coefficient for a passenger vehicle using the instantaneous cornering stiffness. In: 2008 American control conference, Washington, DC, USA, 11–13 June 2008, pp. 4591–4596. New York: IEEE.
24.
ViehweiderANamKFujimotoH. Evaluation of a betaless instantaneous cornering stiffness estimation scheme for electric vehicles. In: 2012 9th France–Japan & 7th Europe–Asia congress on mechatronics and 2012 13th international workshop on research and education in mechatronics, Paris, France, 21–223 November 2012, pp. 272–279. New York: IEEE.
25.
ViehweiderAHoriY. Electric vehicle lateral dynamics control based on instantaneous cornering stiffness estimation and an efficient allocation scheme. In: 7th Vienna international conference on mathematical modelling, Vienna, Austria, 14–17 February 2012, pp. 1213–12186. Laxenburg: IFAC.
26.
LiJZhengYLinZ. Recursive identification of time-varying systems: self-tuning and matrix RLS algorithms. Systems Control Lett2014; 66: 104–110.
27.
EslamianMAlizadehGMirzaeiM. Optimization-based non-linear yaw moment control law for stabilizing vehicle lateral dynamics. Proc IMechE Part D: J Automobile Engineering2007; 221(12): 1513–1523.
28.
KienckeUNielsenL. Automotive control systems for engine, driveline, and vehicle. 2nd edition. Berlin: Springer, 2005.
