• Journal of Mechanical Science and Technology
• /
• v.20 no.11
• /
• pp.1898-1913
• /
• 2006

This paper deals with the design of a yaw rate controller based on gain-scheduled H$\infty$ optimal control, which is intended to maintain the lateral stability of a vehicle. Uncertain factors such as vehicle mass and cornering stiffness in the vehicle yaw rate dynamics naturally call for the robustness of the feedback controller and thus H$\infty$ optimization technique is applied to synthesize a controller with guaranteed robust stability and performance against the model uncertainty. In the implementation stage, the feed-forward yaw moment by driver's steer input is estimated by the disturbance observer in order to determine the accurate compensatory moment. Finally, HILS results indicate that the proposed yaw rate controller can satisfactorily improve the lateral stability of an automobile.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1014-1019
• /
• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

• Journal of Auto-vehicle Safety Association
• /
• v.5 no.1
• /
• pp.62-68
• /
• 2013

This paper describes development of 4 Wheel Drive (4WD) Electric Vehicle (EV) based driving control algorithm for severe driving situation such as icy road or disturbance. The proposed control algorithm consists three parts : a supervisory controller, an upper-level controller and optimal torque vectoring controller. The supervisory controller determines desired dynamics with cornering stiffness estimator using recursive least square. The upper-level controller determines longitudinal force and yaw moment using sliding mode control. The yaw moment, particularly, is calculated by integration of a side-slip angle and yaw rate for the performance and robustness benefits. The optimal torque vectoring controller determines the optimal torques each wheel using control allocation method. The numerical simulation studies have been conducted to evaluated the proposed driving control algorithm. It has been shown from simulation studies that vehicle maneuverability and lateral stability performance can be significantly improved by the proposed driving controller in severe driving situations.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.1126-1131
• /
• 2007

This paper presents unified chassis control (UCC) to improve the vehicle lateral stability. The unified chassis control implies combined control of active front steering (AFS), electronic stability control (ESC) and continuous damping control (CDC). A direct yaw moment controller based on a 2-D bicycle model is designed by using sliding mode control law. A direct roll moment controller based on a 2-D roll model is designed. The computed direct yaw moment and the direct roll moment are generated by AFS, ESP and CDC control modules respectively. A control authority of the AFS and the ESC is determined by tire slip angle. Computer simulation is conducted to evaluate the proposed integrated chassis controller by using the Matlab, simulink and the validated vehicle simulator. From the simulation results, it is shown that the proposed unified chassis control can provide with improved performance over the modular chassis control.

• Journal of Auto-vehicle Safety Association
• /
• v.12 no.3
• /
• pp.13-19
• /
• 2020

This paper presents steering controller for unintended lane departure avoidance under crosswind using vehicle lateral disturbance estimation. Vehicles exposed to crosswind are more likely to deviate from lane, which can lead to accidents. To prevent this, a lateral disturbance estimator and steering controller for compensating disturbance have been proposed. The disturbance affecting lateral motion of the vehicle is estimated using Kalman filter, which is on the basis of the 2-DOF bicycle model and Electric Power Steering (EPS) module. A sliding mode controller is designed to avoid unintended the lane departure using the estimated disturbance. The controller is based on the 2-DOF bicycle model and the vision-based error dynamic model. A torque controller is used to provide appropriate assist torque to driver. The performance of proposed estimator and controller is evaluated via computer simulation using Matlab/Simulink.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.9
• /
• pp.818-826
• /
• 2015

This paper presents a novel methodology for estimating vehicle ego-motion, i.e. tri-axis linear velocities and angular velocities by using stereo vision sensor and 2G1Y sensor (longitudinal acceleration, lateral acceleration, and yaw rate). The estimated ego-motion information can be utilized to predict future ego-path and improve the accuracy of 3D coordinate of obstacle by compensating for disturbance from vehicle movement representatively for collision avoidance system. For the purpose of incorporating vehicle dynamic characteristics into ego-motion estimation, the state evolution model of Kalman filter has been augmented with lateral vehicle dynamics and the vanishing point estimation has been also taken into account because the optical flow radiates from a vanishing point which might be varied due to vehicle pitch motion. Experimental results based on real-world data have shown the effectiveness of the proposed methodology in view of accuracy.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.2
• /
• pp.15-20
• /
• 2012

This study is to propose and develop an integrated dynamics control system to improve and enhance the lateral stability and handling performance. To achieve this target, we integrate an AFS and a 4WS systems with a fuzzy logic controller. The IDCS determines active additional steering angle of front wheel and controls the steering angle of rear wheel. The results show that the IDCS improves the lateral stability and controllability on dry asphalt and snow paved road when double lane change and step steering inputs are applied. Yaw rate of the IDCS vehicle tracks reference yaw rate very well and body slip angle is reduced about by 50%. Response time of the IDCS vehicle is also decreased.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.101.5-101
• /
• 2002

A vehicle simulator is made here to simulate the lateral control of vehicles. Dead-reckoning sensors which consist of gyroscopes and accelerometers are utilized for the positioning of it. A significant side-slip occurs when the developed vehicle is drove autonomously. To cope with the side-slip, the vehicle is steered to follow the reference yaw rate which is generated by the relationship between the target point and the position of vehicle. The experimental results show the good performances of lane tracking and the passenger comfort.

• International Journal of Automotive Technology
• /
• v.8 no.1
• /
• pp.49-57
• /
• 2007

This paper proposes a traction control system (TCS) that uses a sliding mode wheel slip controller and a PID throttle valve controller. In addition, a yaw motion controller (YMC) is also developed to improve lateral stability using a PID rear wheel steering angle controller. The dynamics of a vehicle and characteristics of the controllers are validated using a proposed full-car model. A driver model is also designed to steer the vehicle during maneuvers on a split ${\mu}$ road and double lane change maneuver. The simulation results show that the proposed full-car model is sufficient to predict vehicle responses accurately. The developed TCS provides improved acceleration performances on uniform slippery roads and split ${\mu}$ roads. When the vehicle is cornering and accelerating with the brake or engine TCS, understeer occurs. An integrated TCS eliminates these problems. The YMC with the integrated TCS improved the lateral stability and controllability of the vehicle.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.11
• /
• pp.998-1003
• /
• 2013

This study proposes an adaptive control algorithm for lateral motion of a UGV (Unmanned Ground Vehicle) using an NN (Neural Networks). The lateral motion of the UGV can be corrupted with various uncertainties such as side slip. In order to compensate the performance degradation of the UGV under various uncertainties, an NN-based adaptive control is designed by utilizing a virtual control concept. Since both the drift and input gain terms are uncertain, the proposed method adapts the whole terms related to the difference between the nominal and real systems. To avoid a singularity problem with the adaptive control, the affine property of the UGV dynamic model is utilized and the overall closed-loop stability is analyzed rigorously. Finally, numerical simulations using Carsim are performed to validate the effectiveness of the proposed scheme.