Accurate modelling of an autonomous ground vehicle (AGV) is challenging due to strong nonlinearity, model perturbations and external disturbances. This necessitates the design of path tracking controllers with robustness. In this paper, an integrated control strategy for path tracking and handling stability of AGVs is proposed, leveraging a four-wheel steering (4WS) system. To address the path tracking problem, a fuzzy logic-based steering strategy is developed, which enables parameter self-adjustment based on displacement error. Additionally, a reference model is employed to transform the path tracking problem into a reference signal tracking problem, ensuring the handling stability of vehicles. A sliding mode controller is designed to track the reference signal, utilizing an extended state observer to estimate and compensate for unmodeled dynamics and unknown external disturbances in real-time. Simulation results demonstrate the effectiveness of the proposed strategy in accurately tracking the desired path while maintaining the lateral stability of the vehicle. Furthermore, the strategy exhibits strong robustness against uncertainties and disturbances.
AmerNHHudhaKZamzuriH, et al. Adaptive modified Stanley controller with fuzzy supervisory system for trajectory tracking of an autonomous armoured vehicle. Robot Auton Syst2018; 105: 94–111.
2.
ReinaGMilellaAUnderwoodJ. Self-learning classification of radar features for scene understanding. Robot Auton Syst2012; 60: 1377–1388.
3.
DickmannJKlappsteinJHahnM, et al. Automotive radar the key technology for autonomous driving: from detection and ranging to environmental understanding. In: 2016 IEEE radar conference (RadarConf), Philadelphia, PA, USA, 2–6 May 2016, pp.1–6. New York: IEEE.
4.
WangJZhengHZongC. Longitudinal and lateral dynamics control of automatic lane change system. Trans Inst Meas Contr2019; 41: 4322–4338.
5.
ChenCJiaYShuM, et al. Hierarchical adaptive path-tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst2015; 16: 2900–2912.
6.
Al-MayyahiAWangWBirchP. Design of fractional-order controller for trajectory tracking control of a nonholonomic autonomous ground vehicle. J Contr Autom Electr Syst2016; 27: 29–42.
7.
GuoKGuanH. Modelling of driver/vehicle directional control system. Veh Syst Dyn1993; 22: 141–184.
8.
LeeTKangJYiK, et al. An investigation on the integrated human driver model for closed-loop simulation of intelligent safety systems. J Mech Sci Technol2010; 24: 761–767.
9.
DingHGuoKWanF, et al. An analytical driver model for arbitrary path following at varying vehicle speed. Int J Veh Auton Syst2007; 5: 204–218.
10.
XuSPengH. Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst2020; 21: 48–58.
11.
WangYBianNLiJ, et al. A triple-step non-linear control for path following of autonomous vehicles with uncertain kinematics and dynamics. IET Control Theory Appl2017; 11: 3381–3387.
12.
HuCJingHWangR, et al. Robust H∞ output-feedback control for path following of autonomous ground vehicles. Mech Syst Signal Process2016; 70: 414–427.
13.
HangPChenXLuoF. LPV/H∞ controller design for path tracking of autonomous ground vehicles through four-wheel steering and direct yaw-moment control. Int J Autom Technol2019; 20: 679–691.
14.
JinMLeeJChangPH, et al. Practical nonsingular terminal sliding-mode control of robot manipulators for high-accuracy tracking control. IEEE Trans Ind Electron2009; 56: 3593–3601.
15.
LvSWangNWangY, et al. Nonsingular terminal sliding mode based trajectory tracking control of an autonomous surface vehicle with finite-time convergence. In: CongFLeungAWeiQ (eds) Advances in neural networks-ISNN 2017: 14th international symposium, ISNN 2017. Cham: Springer, 2017, pp.83–92.
16.
WangXLiS. Finite-time trajectory tracking control of under-actuated autonomous surface vessels based on non-singular terminal sliding mode. Aust J Electr Electron Eng2012; 9: 235–246.
17.
WuYWangLZhangJ, et al. Path following control of autonomous ground vehicle based on nonsingular terminal sliding mode and active disturbance rejection control. IEEE Trans Veh Technol2019; 68: 6379–6390.
18.
YoshidaHShinoharaSNagaiM. Lane change steering manoeuvre using model predictive control theory. Veh Syst Dyn2008; 46: 669–681.
19.
FalconePBorrelliFAsgariJ, et al. Predictive active steering control for autonomous vehicle systems. IEEE Trans Control Syst Technol2007; 15: 566–580.
20.
YuSGuoYMengL, et al. MPC for path following problems of wheeled mobile robots. IFAC-PapersOnLine2018; 51: 247–252.
21.
LiuYYuSGuoY, et al. Receding horizon control for path following problems of wheeled mobile robots. Control Theory Appl2017; 34: 424–432.
22.
KuuttiSBowdenRJinY, et al. A survey of deep learning applications to autonomous vehicle control. IEEE Trans Intell Transp Syst2021; 22: 712–733.
23.
MoonSMoonIYiK. Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance. Control Eng Pract2009; 17: 442–455.
24.
ChenJShuaiZZhangH, et al. Path following control of autonomous four-wheel-independent-drive electric vehicles via second-order sliding mode and nonlinear disturbance observer techniques. IEEE Trans Ind Electron2021; 68: 2460–2469.
25.
ShufengWJunyouZ. Performance analysis of 4WS vehicle based on different control strategy. In: 2009 Chinese control and decision conference, Guilin, China, 17–19 June 2009, pp.5401–5404. New York: IEEE.
26.
TanLYuSGuoY, et al. Sliding-mode control of four wheel steering systems. In: 2017 IEEE international conference on mechatronics and automation (ICMA), Takamatsu, Japan, 6–9 August 2017, pp.1250–1255. New York: IEEE.
27.
ShiKYuanXHeQ. Double-layer dynamic decoupling control system for the yaw stability of four wheel steering vehicle. Int J Control Autom Syst2019; 17(5): 1255–1263.
28.
XuFXLiuXHChenW, et al. Improving handling stability performance of four-wheel steering vehicle based on the H2/H∞ robust control. Appl Sci2019; 9(5): 857.
29.
YuSLiWWangW, et al. Nonlinear control of active four wheel steer-by-wire vehicles. IEEE Access2019; 7: 127117–127127.
30.
PriyankaEBMaheswariCThangavelS. Online monitoring and control of flow rate in oil pipelines transportation system by using PLC based fuzzy-PID controller. Flow Meas Instrum2018; 62: 144–151.
31.
JinXChenKZhaoY, et al. Simulation of hydraulic transplanting robot control system based on fuzzy PID controller. Measurement2020; 164: 108023.
32.
NagaiMShinoMGaoF. Study on integrated control of active front steer angle and direct yaw moment. JSAE Rev2002; 23: 309–315.
33.
YangXWangZPengW. Coordinated control of AFS and DYC for vehicle handling and stability based on optimal guaranteed cost theory. Veh Syst Dyn2009; 47: 57–79.
34.
LiuCSunWZhangJ. Adaptive sliding mode control for 4-wheel SBW system with Ackerman geometry. ISA Trans2020; 96: 103–115.
35.
ZhengHYangSLiB. Optimization control for 4WIS electric vehicle based on the coincidence degree of wheel steering centers. SAE Int J Veh Dyn Stab NVH2018; 2(3): 169–184.
36.
RussellHEBGerdesJC. Design of variable vehicle handling characteristics using four-wheel steer-by-wire. IEEE Trans Control Syst Technol2016; 24: 1529–1540.
37.
Sira-RamirezHOliver-SalazarMA. On the robust control of buck-converter DC-motor combinations. IEEE Trans Power Electron2013; 28: 3912–3922.
38.
ChenJIEPattonRJZhangHY. Design of unknown input observers and robust fault detection filters. Int J Control1996; 63: 85–105.
39.
ChenMSHwangYRTomizukaM. A state-dependent boundary layer design for sliding mode control. IEEE Trans Automat Contr2002; 47: 1677–1681.
40.
YuSFengYYangX. Extended state observer–based fractional order sliding-mode control of piezoelectric actuators. Proc IMechE, Part I: J Systems and Control Engineering2021; 235: 39–51.
41.
EsmailzadehEVossoughiGRGoodarziA. Dynamic modeling and analysis of a four motorized wheels electric vehicle. Veh Syst Dyn2001; 35: 163–194.
