Validation of the 6-dof vehicle dynamics model and its related VBA program under the constant radius turn manoeuvre

Yang, Seung Min; Kim, J. H.

doi:10.1007/s12239-012-0057-9

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The state space model is based on the vehicle 6-DOF sprung mass model [ 31 , 32 ] for expressing the six-dimensional acceleration and angular velocity. The dynamics are composed based on the Euler rigid body equation and the force or moment of each axis which is given by each dynamic [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter

Park

2021

Sensors

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The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

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Section: Methodsmentioning

confidence: 99%

Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter

Park

2021

Sensors

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“…On the other hand, highly complex models have been derived which describe one aspect or several aspects of the vehicle. 6-DOF models which describe the full vehicle dynamic behavior serve as useful simulation tools in [2], [3], [4], [5]. Also simpler models which omit the suspension are also common [6].…”

Section: Contributors and Funding Sourcesmentioning

confidence: 99%

Nonlinear Control of a Ground Vehicle using Data-Driven Dynamic Models

Hassen

Chour

Weaver

et al. 2020

SAE Technical Paper Series

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As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses. Sliding mode control layered on top of pure pursuit guidance is then used to drive the vehicle for waypoint following, using the empirical model. Simulation and experimental results show that the vehicle can perform waypoint following for a circular and sinusoidal with minimal error. Furthermore, more experimental data was collected to show the effects of adaptive velocity and adaptive lookahead for path tracking. A comparison of the controller's performance was also explored between on-road and off-road terrain.

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“…As the friction coefficient of road surface increasing, roll stability became another research hotspot [28]. To accurately predict the roll motion of vehicle bodies, some multiple degrees-of-freedom vehicle dynamics models were proposed to replace the typical bicycle model, which is unable to describe large variations in lateral acceleration [29,30]. On this basis, two different rollover prevention modes have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads

et al. 2020

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To contribute to the development of sustainable transport that is safe, eco-friendly, and efficient, this research proposed a safe and ecological speed control system for heavy-duty vehicles on long–steep downhill and sharp-curved roads under a partially connected vehicles environment consisting of connected heavy-duty vehicles (CHDVs) and conventional human-driven vehicles. This system prioritizes braking and lateral motion safety before improving fuel efficiency and ensuring traffic mobility at optimal status, and optimizes the speed trajectories of CHDVs to control the entire traffic. Speed optimization is modelled as an optimal control problem and solved by the iterative Pontryagin’s maximum principle algorithm. The simulation-based evaluation shows that the proposed system effectively reduces the peak temperature of the brake drums, the lateral slip angle of the vehicle wheels, and the lateral load transfer rate of the vehicle body; all these measurements of effectiveness are limited to safe ranges. A detailed investigation reveals that the proposed system reduces fuel consumption by up to 15.49% and inhibits the adverse effects on throughput. All benefits increase with the market penetration rate (MPR) of CHDVs and the traffic congestion level and reach significant levels under low MPRs of CHDVs. This indicates that the proposed system has good robustness for the impedance from conventional vehicles and could be implemented in the near future.

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Validation of the 6-dof vehicle dynamics model and its related VBA program under the constant radius turn manoeuvre

Cited by 6 publications

References 11 publications

Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter

Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter

Nonlinear Control of a Ground Vehicle using Data-Driven Dynamic Models

Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads

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