Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope

Cui, Qingjia; Ding, Ran; Wei, Chongfeng

doi:10.1080/00423114.2020.1776344

Cited by 50 publications

(28 citation statements)

References 37 publications

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“…In this section, two different kinds of models with front-wheel steering (model A) and four-wheel steering modes (model B) were validated in terms of their ability to retain their track trajectory. Compared with the classical dynamic models used to perform the trajectory keeping and lateral stability, 18,21 the proposed spatial vehicle dynamics model comprehensively considers the motion coupling effects of a six-DOF vehicle, such as the coupling effects between different vehicle subsystems and the road irregularity, which allows the model to perform the dynamic characteristics more realistic during operation. Therefore, the main advantage of the established dynamic model is that it reveals the dynamic performance of the novel vehicle system.…”

Section: Dynamic Response Analysis Of Vehiclementioning

confidence: 99%

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Establishment and comparison of a spatial dynamics model for virtual track train with different steering modes

Yin

Zhang

Lu³

2021

Proceedings of the Institution of Mechanical Engineers, Part K:

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To solve the urbanization and the economic challenges, a virtual track train (VTT) transportation system has been proposed in China. To evaluate the dynamic behavior of the VTT, a spatial dynamics model has been developed that considers the suspension system and the steering system. Additionally, the model takes into account road irregularity to make simulations more realistic. Based on the newly proposed dynamic model and a designed proportional–integral–derivative (PID) controller, simulation frames of the vehicle and of the VTT are established with the path-tracking performance. The results show that the vehicle and the VTT can run along a desired lane with allowable errors, verifying the proposed model. The vehicle and VTT with the four-wheel steering system show a better dynamic performance than the models with the front-wheel steering system in the curved section. Moreover, the simulation frame can be further applied to dynamics-related assessments, parameter optimization and active suspension control strategy.

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Section: Dynamic Response Analysis Of Vehiclementioning

confidence: 99%

“…Cui et al. 21 proposed a path following controller with steering angle envelopes based on model predictive control, which adopted a two -DOF vehicle model. Ghike et al.…”

Section: Introductionmentioning

confidence: 99%

Establishment and comparison of a spatial dynamics model for virtual track train with different steering modes

Yin

Zhang

Lu³

2021

Proceedings of the Institution of Mechanical Engineers, Part K:

View full text Add to dashboard Cite

show abstract

“…However, they do not consider the dynamic performance of the vehicle, and generally have a good control effect at low speed and some simple conditions. To adapt to the complex and changeable driving conditions of autonomous vehicles, considering the nonlinear dynamic characteristics, the path tracking control method based on dynamics has been widely studied 12,13 . The path tracking control method based on dynamics often takes the traditional linear two-degrees-of-freedom(2-DOF) model as the reference model 12,13 , and the control algorithms are PID algorithm 14 , model prediction algorithm 15 , sliding mode algorithm 16 , reinforcement learning control 17 .…”

Section: Introductionmentioning

confidence: 99%

Multi-directional-motion-coupling-based Extreme Motion Control of Distributed Drive Autonomous Vehicle

Ding

Wang

Yang

et al. 2022

Preprint

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To improve the multi-directional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the motion control law design method based on vehicle multi-directional motion coupling dynamic model are proposed. The stable driving state of DDAVs is identified by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then the extreme speed of stable driving is estimated. The eight-degrees-of-freedom(8-DOF) DDAVs vehicle dynamics model is used to design the control law of steering wheel angle, wheel torque and active suspension actuation force. Based on the above methods and sliding mode algorithm, the Multi-directional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method.

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“…To adapt to the automatic vehicle’s complicated and variable driving conditions and consider the nonlinear dynamic characteristics of automatic driving vehicles, the path-based path tracking control method has been extensively investigated 11 , 12 . The dynamic path tracking control method often uses a conventional linear two-degrees-of-freedom (2-DOF) model as a reference model 13 , 14 , and the employed control algorithm includes the PID algorithm 15 , model prediction algorithm 16 , sliding mode control 5 , and reinforcement learning algorithm 17 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic-boundary-based lateral motion synergistic control of distributed drive autonomous vehicle

Wang

Ding

Yang

et al. 2021

Sci Rep

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To improve the path tracking accuracy and yaw stability of distributed drive autonomous vehicles (DDAVs) under extreme working conditions, a cooperative lateral motion control method based on the dynamic boundary is proposed to prevent different road adhesion conditions from affecting the motion stability of DDAVs. Based on the analysis of the DDAV lateral dynamics system coordination mechanism, a dynamic boundary considering the pavement adhesion coefficient is proposed, and the Lateral Motion Synergistic Control System (LMSCS) is designed. The LMSCS is divided into the coordination, control, and executive layers. The coordination layer divides the control domain into the stable, quasi-stable, and unstable domains by the dynamic boundary, and coordinates the control strength of the path following control and yaw stability control. In the control layer, the path following control and yaw stability control laws are designed based on the global fast terminal sliding mode. The executive layer estimates the expected steering wheel angle and expected additional wheel torque. Joint simulations under double line shifting conditions confirmed that LMSCS reflects the impact of the road attachment conditions and improves the path tracking accuracy and vehicle yaw stability. The LMSCS has better overall performance than existing lateral motion control methods.

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Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope

Cited by 50 publications

References 37 publications

Establishment and comparison of a spatial dynamics model for virtual track train with different steering modes

Establishment and comparison of a spatial dynamics model for virtual track train with different steering modes

Multi-directional-motion-coupling-based Extreme Motion Control of Distributed Drive Autonomous Vehicle

Dynamic-boundary-based lateral motion synergistic control of distributed drive autonomous vehicle

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