• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.353-356
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• 2002

In vehicle accidents, the rolling, pitching, and yawing which are produced by collisions affect the motions of vehicle. Therefore, vehicle behavior under impact situation should be analyzed in three-dimension. In this study, three-dimensional vehicle dynamic equations based on impulse-momentum conservation principles under vehicle impact are introduced for simulation. This analysis has been performed by the real vehicle impact data from JARI and RICSAC. This study suggested each system modeling such as suspension, steering, brake and tire as well as the appropriate vehicle behavior simulation model with respect to pre and post impact.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.76-82
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• 2005

In this paper, validation of Driver Steering Model has been conducted. The comparison between the simulation model and vehicle test results shows that the model is very feasible for describing combined human driver and actual vehicle dynamic behaviors. The 3D vehicle model is consisted of 6-DOF sprung mass and 4-quarter car model for vehicle body dynamics. Powertrain model including differential gear and Pacejka tire model are applied. The driver steering model is also validated with vehicle test result. The driver steering model is based on angle and displacement error from the desired path, recognized by driver.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.91-99
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• 2003

When the driver suddenly depresses the brake pedal under critical conditions, the desired trajectory of the vehicle can be changed. In this study, the vehicle dynamics and fuzzy logic controller are used to control the vehicle trajectory. The dynamic vehicle model consists of the engine, the rotational wheel, chassis, tires and brakes. The engine model is derived from the engine experimental data. The engine torque makes the wheel rotate and generates the angular velocity and acceleration of the wheel. The dynamic equation of the vehicle model is derived from the top-view vehicle model using Newton's second law. The Pacejka tire model formulated from the experimental data is used. The fuzzy logic controller is developed to compensate for the trajectory error of the vehicle. This fuzzy logic controller individually acts on the front right, front left, rear right and rear left brakes and regulates each brake torque. The fuzzy logic controlling each brake works to compensate for the trajectory error on the split - $\mu$ road conditions follows the desired trajectory.

• The Journal of Korea Robotics Society
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• v.13 no.3
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• pp.174-181
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• 2018

For a mobile robot that travels along a terrain consisting of various geology, information on tire force and friction coefficient between ground and wheel is an important factor. In order to estimate the lateral force between ground and wheel, a lot of information about the model and the surrounding environment of the vehicle is required in conventional method. Therefore, in this paper, we are going to estimate lateral force through simple model (Minimal Argument Lateral Slip Curve, MALSC) using only minimum data with high estimation accuracy and to improve estimation reliability of the friction coefficient by using the estimated lateral force data. Simulation is carried out to analyze the correlation between the longitudinal and transverse friction coefficients and slip angles to design the simplified lateral force estimation model by analysing simulation data and to apply it to the actual field environment. In order to verify the validity of the equation, estimation results are compared with the conventional method through simulation. Also, the results of the lateral force and friction coefficient estimation are compared from both the conventional method and the proposed model through the actual robot running experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.139-147
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• 2004

The purpose of engine control TCS is to regulate engine torque to keep driven wheel slip in a desired range. In this paper, engine control TCS using sliding mode control law based on engine model and estimated load torque is proposed. This system includes a two-level controller. Slip controller calculates desired wheel torque, and engine torque controller determines throttle angle for engine torque corresponding to desired wheel torque. Another issue is to measure load torque for model based controller design. Luenberger observer with state variables of load torque and engine speed solves this problem as estimating load torque. The performance of controller and observer is certificated by simulation using 8-degree vehicle model, Pacejka tire model, and 2-state engine model. The simulation results in various maneuvers during slippery and split road conditions showed that acceleration performance and ability of the vehicle with TCS is improved. Also, the load torque observer could estimate real load torque very well, so its performance was proved.