Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

Chen, Xiang; Peng, Haonan; Wang, Weida; Zhang, Lipeng; An, Quan; Cheng, Shuo

doi:10.1177/0954407020946884

Cited by 30 publications

(24 citation statements)

References 32 publications

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“…Due to its simplicity, the yaw plane vehicle model considering the lateral movement, longitudinal movement and heading angle change of the vehicle is often used in the literature [13,17,18,20]. The yaw plane dynamics vehicle model is established in this paper, as shown in Figure 2.…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

“…However, these algorithms only provide the vehicle with the ability to perform direction and speed control under good road adhesion conditions, but they seldom consider the lateral stability under extreme conditions. When ADVs vehicle travels on a road with poor adhesion conditions and high vehicle speed, the vehicle may become unstable and drive out and into the target trajectory [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The controller effectively realizes trajectory tracking with high accuracy and lateral stability and strong robustness. Similarly to Reference [15], Reference [13] adopted the method of multi-objective control, using sideslip angle and yaw rate as the reference input of stability control target and vehicle speed; and yaw angle and lateral displacement as maneuverability and tracking accuracy, respectively. The main innovation of this approach is to transform the constraint problem of the lateral motion state of the vehicle into the planning and tracking problem of the lateral motion state.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Tang

Wang

et al. 2021

Electronics

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For distributed drive autonomous vehicles, adding lateral stability control (LSC) to the trajectory tracking control (TTC) can optimize the distribution of the driving torque of each wheel, so that the vehicle can track the planned trajectory while maintaining stable lateral motion. However, the influence of adding LSC on the TTC system is still unclear. Firstly, a stability-track hierarchical control structure composed of LSC and TTC was established, and the interaction between the two layers was identified as the key of this paper. Then, the Intrinsic Mechanistic framework of the stability-tracking control (STC) was proposed by establishing and analyzing the vehicle dynamic model and control process of two layers. Finally, through simulation experiments, it was found that the change in the curvature of the target trajectory will make the tracking target trajectory and maintaining the lateral stability of the vehicle appear to conflict; in addition, in the LSC layer, the steering characteristics and delay characteristics of different reference models have a greater impact on the lateral stability and trajectory tracking performance; moreover, adjusting the preview time has a more obvious effect on trajectory tracking and lateral stability than the stability correction intensity coefficient.

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Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Tang

Wang

et al. 2021

Electronics

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“…In Ref. 33 , a hierarchical path tracking coordinated control with DYC is proposed, of which the upper controller uses the MPC based on the dynamic adjustment of control target weight coefficient by variance adjustment factor to calculate the traction force, front wheel steering angle and yaw moment required by the vehicle in the double lane change manoeuvres.…”

Section: Introductionmentioning

confidence: 99%

Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle

Barari

Afshari

Liang

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Coordination of Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) has been widely used for non-autonomous vehicle lateral stability control. Recently, some researchers used it (AFS/DYC) for path-following of autonomous vehicles. However, current controllers are not robust enough with respect to uncertainties and different road conditions to guarantee lateral stability of Autonomous Four In-wheel Motor Drive Electric Vehicles. Thus, a coordinated control is proposed to address this issue. In this paper, a two-layer hierarchical control strategy is utilized. In the upper-layer, a self-tunable super-twisting sliding mode control is utilized to deal with parametric uncertainties, and a Model Predictive Control (MPC) is used in order to allocate the control action to each AFS and DYC. Parametric uncertainties of tires’ cornering stiffness, vehicle mass and moment of inertia are considered. Simulations with different road conditions for path-following scenario have been conducted in MATLAB/Simulink. An autonomous vehicle equipped with Four In-wheel Motor and two degrees of freedom vehicle dynamics model is used in this study. In the end, the performance of the proposed controller is compared with the MPC controller. Simulation results reveal that the proposed controller provides better path-following in comparison with the MPC controller.

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“…28 As mentioned in our past published work, 29 the motion tracking based on the idea of decoupling has been realized. Aiming at the distributed drive electric vehicle which offers more controllable inputs to improve the driving stabilization, 30,31 the inverse system is adopted to decouple the longitudinal, lateral, and yaw motions of dynamics, then the decoupled system followed the reference velocity, lateral position, and yaw profile to achieve motion tracking. With the motion coupling has been overcome, another problem unfortunately appears, that is, the accumulation of vehicle states tracking errors causes the stable yaw error and the lateral tracking divergence.…”

Section: Introductionmentioning

confidence: 99%

Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

Liang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

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Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

Cited by 30 publications

References 32 publications

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle

Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

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