High-speed lateral stability and trajectory tracking performance for a tractor-semitrailer with active trailer steering

Hou, Yu; Xu, Xiaomei

doi:10.1371/journal.pone.0277358

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…As introduced in the above chapter, the acceleration of the loading stage is achieved based on the prediction of the failure time node during this stage. At present, the residual life prediction and some related health monitoring research has been carried out based on various kinds of models and methods [28][29][30][31][32], but the object of prediction among these researches are usually the working life or some other parameters, not the fatigue life. In the published related studies, most of the fatigue life predictions are carried out based on the S-N curve of the material.…”

Section: The Residual Fatigue Life Prediction Approachmentioning

confidence: 99%

Research of the crankshaft high cycle bending fatigue experiment design method based on the modified unscented Kalman filtering algorithm and the SAFL approach

Rui,

Jiang,

Sun

et al. 2023

PLoS ONE

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In modern engineering application, enough high cycle bending fatigue strength is the necessary factor to provide the basic safety security for the application of the crankshaft in automobile engines (both diesel and gasoline types). At present, this parameter is usually obtained through the standard bending fatigue experiment process, which is time consuming and expensive. In this paper, a new accelerated crankshaft bending fatigue experiment was proposed step by step. First the loading procedure was quickened through the prediction of the residual fatigue life based on the UKF (unscented Kalman filtering algorithm). Then the accuracy of the predictions was improved based on the modified sampling range and the theory of fracture mechanics. Finally the statistical analysis method of the fatigue limit load was performed based on the above predictions. The main conclusion of this paper is that the proposed accelerated bending fatigue experiment can save more than 30% of the bending fatigue experiment period and provide nearly the same fatigue limit load analysis result. In addition, compared with the particle filtering algorithm method, the modified UKF can provide much higher accuracy in predicting the residual bending fatigue life of the crankshaft, which makes this method more superior to be applied in actual engineering.

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Section: The Residual Fatigue Life Prediction Approachmentioning

confidence: 99%

Research of the crankshaft high cycle bending fatigue experiment design method based on the modified unscented Kalman filtering algorithm and the SAFL approach

Rui,

Jiang,

Sun

et al. 2023

PLoS ONE

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show abstract

“…The function of control involves using suitable controllers to guide the vehicle along the planned path [ 12 , 13 ]. This study primarily focuses on the study of path planning and tracking control [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

Zhang,

Liu,

Gao

et al. 2023

Sensors

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Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.

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“…Various control theories have been applied to the stability control of distributed drive electric vehicles, including the proportional integral differential PID [ 5 , 6 ], linear quadratic regulator (LQR) [ 7 , 8 ], fuzzy control [ 9 , 10 ], sliding mode control (SMC) [ 11 , 12 ], and model predictive control (MPC) [ 13 , 14 , 15 ]. Researchers have utilized these control theories for direct yaw torque control.…”

Section: Introductionmentioning

confidence: 99%

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Gao,

Zhao,

Zhang

2023

Sensors

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With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle’s stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

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High-speed lateral stability and trajectory tracking performance for a tractor-semitrailer with active trailer steering

Cited by 15 publications

References 34 publications

Research of the crankshaft high cycle bending fatigue experiment design method based on the modified unscented Kalman filtering algorithm and the SAFL approach

Research of the crankshaft high cycle bending fatigue experiment design method based on the modified unscented Kalman filtering algorithm and the SAFL approach

Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

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