Research on autonomous vehicle path tracking control considering roll stability

Lin, Fanghua; Wang, Shaobo; Zhao, Youqun; Cai, Yizhang

doi:10.1177/0954407020942006

Cited by 37 publications

(23 citation statements)

References 34 publications

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“…To avoid accidents in road tests, simulation tests are carried out to evaluate the performance of the proposed controller. It is noted that the conventional SMC without considering system perturbations is given by (19) and (20). The vehicle parameters are given in Table Ⅰ.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Linear, nonlinear, and optimization control methods were employed by many studies on model-based controllers for trajectory following [3,[19][20][21][22][23][24][25][26][27][28][29][30][31]. The famous Stanley steering control law was designed based on the non-linear feedback function of lateral deviation, and exponential convergence is obtained under some assumptions in [1].…”

Section: Introductionmentioning

confidence: 99%

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Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

Ao¹,

Huang²,

Wong³

et al. 2021

IEEE Access

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This paper focuses on the path following control problems for autonomous driving vehicles. Aiming at enhancing the robustness and attenuating the chattering phenomenon, a super-twisting sliding mode control algorithm (STA) is developed based on Lyapunov theory, where the proof of the stability of the control system is presented by applying the backstepping technique. Moreover, co-simulation between Matlab/Simulink and Carsim is carried out to verify the path following control performance. In this research, Stanley controller, conventional sliding mode control (SMC), and model predictive control (MPC) are used as the benchmark controllers for evaluating the proposed STA performance. Two driving scenarios are considered in the simulations, including normal driving and fierce driving. To comprehensively assess the control performance and control effort (i.e. magnitude of steering), an integrated and weighted performance evaluation index is novelly provided. Simulation results show that the of the proposed STA can be reduced by 40.5%, 25.8%, 10.9% in the normal driving scenario; and 62.5%, 24%, 6.8% in the fierce driving scenario as compared with Stanley controller, conventional SMC, and MPC, respectively. The results also indicate that the proposed STA outperforms the conventional SMC in terms of the chattering attenuation, resulting in a smoother front steering wheel angle input and a smoother yaw rate performance. As compared with MPC, the advantage of the proposed STA lies in its much lower computational complexity. Furthermore, the robustness of the controllers is verified by changing the vehicle mass and tire parameters. The proposed STA can reduce the fluctuation of the by 22.6%, 22.3%, and 5.9% compared with the benchmark approaches. These results imply that the consideration of system perturbations is very critical in the design of the super-twisting sliding mode controller which can improve the robustness of the autonomous vehicle path following system. INDEX TERMSPath following control, super-twisting sliding mode, backstepping, robustness, perturbations.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

Ao¹,

Huang²,

Wong³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…V I S = By placing V v S , V _ v S and V I S , respectively, from equations (2), ( 16) and (17) in the equation ( 15), equation ( 18) is obtained:…”

Section: Modeling and Equationsmentioning

confidence: 99%

“…Alipour et al 16 studied on the lateral stability of the vehicle on slippery roads by computer simulations and they examined the stable motion of the vehicle by calculating wheel forces. Lin et al 17 by computer simulation improved the yaw dynamic for a linear 3-DOF vehicle model under the front wheel angle, sideslip angle and tire slip angles. Chen et al 18 simulated the multi-body dynamic models for study on the rollover characteristics such as rollover speed thresholds of the vehicle in roundabouts.…”

Section: Introductionmentioning

confidence: 99%

The influence of engine gyroscopic moments on vehicle transient handling

Shahabi

Kazemian

Farahat

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This study presents the dynamics of a 15-DOF model of the vehicle by performing simulations to investigate the vehicle handling dynamics in J-turn maneuver. Using Newton’s equations of motion, the equations of motion for the sprung and unsprung masses are all written in the vehicle coordinate system and the tire is modeled with the Pacejka 89 model. Since the engine crankshaft has a rotation relative to the vehicle coordinate system, in order to investigate the effect of the engine gyroscopic moments on the vehicle handling dynamics, the effect of the crankshaft rotation on the torque vector of external forces is considered. The direction of crankshaft rotation can change the direction of engine rotation in the direction of the wheels rotation or in the opposite direction of their rotation, which causes some changes in the gyroscopic torque vector of the engine. Due to the rotation speed of the crankshaft and its moment of inertia, the gyroscopic moments which resulted from the angular momentum of the engine crankshaft are considerable. These gyroscopic moments are added to the torque equation of external forces in the vehicle coordinate system and affect the vehicle handling dynamics. By using the numerical method of Newmark, vehicle’s dynamic behavior is investigated and the validation of its dynamic behavior is done by ADAMS/Car software. This study shows that in transverse engine, if the direction of engine rotation is in the opposite direction of the wheels, the vehicle handling dynamics is improved.

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“…As the core content of the lane keeping system, the lane keeping control algorithm can directly affect the control effect, which has been studied by many scholars [5]. The existing lane keeping control methods can be summarized into the following three types: deviation-based feedback control, model-based feedback control and end-to-end decision control [6][7][8]. Since the deviation-based feedback control does not consider the characteristics of the control system model, the control quantity often does not align with the actual situation, and it is not effective in dealing with high-order nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

Tang

Jiang

et al. 2022

Machines

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In order to improve the adaptability of the lane keeping control system to complex environments, a dynamic lane tracking control strategy of the commercial vehicle based on the robust model predictive control (RMPC) algorithm is proposed considering the state of the preceding vehicle. An RMPC controller is designed with path deviation and control increment as the objective function. The model predictive control problem is transformed into a min–max optimization problem. The linear matrix inequality (LMI) is used for the optimal solution to obtain the optimal control quantity. The strategy to improve the safety and comfort dynamically in the process of lane keeping is designed by adjusting the weight coefficient matrix of RMPC based on fuzzy theory. The results of the simulation and HiL test show that the RMPC controller can meet the requirement of adjusting the lane tracking process dynamically according to the state of the preceding vehicle, which keeps the balance between safety and comfort.

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Research on autonomous vehicle path tracking control considering roll stability

Cited by 37 publications

References 34 publications

Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

Robust Backstepping Super-Twisting Sliding Mode Control for Autonomous Vehicle Path Following

The influence of engine gyroscopic moments on vehicle transient handling

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

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