There is a certain conflict between vehicle stability and the performance of path-tracking under emergency conditions. In this paper, to make full use of the lateral capacity of vehicles, a path tracking method with switchable stability constraints is proposed. First, using the Lyapunov energy equation and Hurwitz’s theorem, the lateral stability region (SR) and the critical steering angle of steady steering at different longitudinal speeds are calculated. Based on this, the extended stability region (ESR) and the extended critical steering angle of the vehicle under the control of an additional yaw moment are obtained. Furthermore, the driving area during collision avoidance is divided into unsafe and safe areas, and based on the model predictive control strategy, a path-tracking controller with switchable stability constraints is established. In the unsafe area, the vehicle states are allowed to break through the SR temporarily, and to ensure path-tracking accuracy, the ESR is taken as the stability constraint. In the safe area, the stability constraint is switched to the SR, and to make the vehicle states quickly return to SR, a regain stability controller is established. Finally, a Simulink-CarSim simulation and a hardware-in-the-loop (HIL) experiment are conducted. The simulation results show that a balance between tracking effect and vehicle stability can be effectively achieved through the proposed path-tracking system under the investigated emergency conditions. The real-time performance of the proposed control strategy is validated through the HIL experiment.
ThrunSMontemerloMDahlkampH, et al. Stanley: the robot that won the DARPA Grand Challenge. J Field Rob2006; 23: 661–692.
2.
FeracoSLucianiSBonfittoA, et al. A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm. In: 2020 AEIT international conference of electrical and electronic technologies for automotive (AEIT AUTOMOTIVE), Turin, Italy, 2020, pp.1–6. IEEE.
3.
ElbanhawiMSimicMJazarR.Receding horizon lateral vehicle control for pure pursuit path tracking. J Vib Control2018; 24: 619–642.
4.
AhnJShinSKimM, et al. Accurate path tracking by adjusting look-ahead point in pure pursuit method. Int J Automot Technol2021; 22: 119–129.
5.
ParkM-WLeeS-WHanW-Y. Development of lateral control system for autonomous vehicle based on adaptive pure pursuit algorithm. In: 2014 14th international conference on control, automation and systems (ICCAS 2014), Gyeonggi-Do, Korea (South), 2014, pp.1443–1447. IEEE .
6.
LuZShyrokauBBoulkrouneB, et al. Performance benchmark of state-of-the-art lateral path-following controllers. In: 2018 IEEE 15th international workshop on advanced motion control (AMC), Tokyo, Japan, 2018, pp.541–546. IEEE.
7.
YuanXHuangGShiK.Improved adaptive path following control system for autonomous vehicle in different velocities. IEEE Trans Intell Transp Syst2020; 21: 3247–3256.
8.
WangX-WPengH-JLiuJ, et al. Optimal control based coordinated taxiing path planning and tracking for multiple carrier aircraft on flight deck. Def Technol2022; 18: 238–248.
9.
LiuJJayakumarPSteinJL, et al. A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments. Veh Syst Dyn2018; 56: 853–882.
10.
XueZJLiLZhongZH, et al. Path tracking control of autonomous ground vehicles via model predictive control and deep deterministic policy gradient algorithm. In: 2021 IEEE intelligent vehicles symposium (IV), Nagoya, Japan, 2021, pp.1220–1227.
11.
RokonuzzamanMMohajerNNahavandiS.Effective adoption of vehicle models for autonomous vehicle path tracking: a switched MPC approach. Veh Syst Dyn2023; 61: 1236–1259.
12.
JeongY. Path tracking control for four-wheel-steering autonomous vehicles based on adaptive sliding mode control with control allocation. In: 2021 21st international conference on control, automation and systems (ICCAS), Jeju, Korea, 2021, pp.1741–1746. IEEE.
13.
ZhouXWangZShenH, et al. A biquadratic-lyapunov-function-based adaptive control methodology with application to automated ground vehicle path tracking. In: 2022 American Control Conference (ACC), Atlanta, GA, 2022, pp.1703–1708. IEEE.
14.
ShamseldinMA.An efficient single neuron PID – sliding mode tracking control for simple electric vehicle model. J Appl Nonlinear Dyn2022; 11: 1–15.
15.
CollottaMFerreroRGiustoE, et al. A fuzzy control system for energy-efficient wireless devices in the Internet of vehicles. Int J Intell Syst2021; 36: 1595–1618.
16.
ArifinBSupraptoBYPrasetyowatiSAD, et al. Steering control in electric power steering autonomous vehicle using type-2 fuzzy logic control and PI control. World Electr Veh J2022; 13: 53.
17.
LiHLiJSuZ, et al. Research on active obstacle avoidance control strategy for intelligent vehicle based on active safety collaborative control. IEEE Access2020; 8: 183736–183748.
18.
HeXLiuYLvC, et al. Emergency steering control of autonomous vehicle for collision avoidance and stabilisation. Veh Syst Dyn2019; 57: 1163–1187.
19.
SunWWangXZhangC.A model-free control strategy for vehicle lateral stability with adaptive dynamic programming. IEEE Trans Ind Electron2020; 67: 10693–10701.
20.
ChengSLiLGuoH-Q, et al. Longitudinal collision avoidance and lateral stability adaptive control system based on MPC of autonomous vehicles. IEEE Trans Intell Transp Syst2020; 21: 2376–2385.
21.
WangHLiuB.Path planning and path tracking for collision avoidance of autonomous ground vehicles. IEEE Syst J2022; 16: 3658–3667.
22.
JiJKhajepourAMelekWW, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans Veh Technol2017; 66: 952–964.
23.
CuiQDingRWeiC, et al. Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope. Veh Syst Dyn2021; 59: 1672–1696.
24.
BalachandranABrownMErlienSM, et al. Predictive haptic feedback for obstacle avoidance based on model predictive control. IEEE Trans Autom Sci Eng2016; 13: 26–31.
25.
ErlienSMFujitaSGerdesJC.Shared steering control using safe envelopes for obstacle avoidance and vehicle stability. IEEE Trans Intell Transp Syst2016; 17: 441–451.
26.
FunkeJBrownMErlienSM, et al. Collision avoidance and stabilization for autonomous vehicles in emergency scenarios. IEEE Trans Control Syst Technol2017; 25: 1204–1216.
27.
LiSLiZYuZ, et al. Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control. IEEE Access2019; 7: 132074–132086.
28.
LiuZChengSJiX, et al. A hierarchical anti-disturbance path tracking control scheme for autonomous vehicles under complex driving conditions. IEEE Trans Veh Technol2021; 70: 11244–11254.
29.
ChenYChenSRenH, et al. Path tracking and handling stability control strategy with collision avoidance for the autonomous vehicle under extreme conditions. IEEE Trans Veh Technol2020; 69: 14602–14617.
30.
WangFYinGRenY, et al. Path tracking of distributed drive electric vehicle based on stability region. In: 2021 27th international conference on mechatronics and machine vision in practice (M2VIP), Shanghai, China, 2021. IEEE.
31.
RajamaniR.Vehicle dynamics and control. New York: Springer Science & Business Media, 2011.
32.
SamsundarJHustonJC.Estimating lateral stability region of a nonlinear 2 degree-of-freedom vehicle. SAE technical paper series 981172. SAE International, 1998.
33.
YanYGXuHGLiuHF.Estimating vehicle stability region based on energy function. Discrete Dyn Nat Soc2015; 2015: 1–7.
34.
GuiggianiM.The science of vehicle dynamics: handling, braking, and ride of road and race cars. New York: Springer, 2014.
35.
NilssonJFalconePAliM, et al. Receding horizon maneuver generation for automated highway driving. Control Eng Pract2015; 41: 124–133.
36.
MattingleyJBoydS.CVXGEN: a code generator for embedded convex optimization. Optim Eng2012; 13: 1–27.
37.
AsiabarANKazemiR.A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control. Proc IMechE, Part K: J Multi-Body Dynamics2019; 233: 549–567.
38.
CaoHSongXZhaoS, et al. An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle. Veh Syst Dyn2017; 55: 1143–1188.
