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Technical Paper

Controller Design for Path Tracking of Autonomous Vehicle Incorporating Four-Wheel Steering System

2022-03-29
2022-01-0299
This research aims to model and assess autonomous vehicle controller while including a four-wheel steering and longitudinal speed control. Such a modeling process simulates human driver behavior with consideration of real vehicle dynamics’ characteristics during standard maneuvers. However, a four-wheel steering control improves vehicle stability and maneuverability as well. A three-degree of freedom bicycle model, lateral deviation, yaw angle, and longitudinal speed is constructed to describe vehicle dynamics’ behavior. Moreover, a comprehensive traction model is implemented which includes an engine, automatic transmission, and non-linear magic formula tire model for simulation of vehicle longitudinal dynamics. A combination of proportional integral derivative (PID) longitudinal controller and fuzzy lateral controller are implemented simultaneously to track the desired vehicle path while minimizing lateral deviation and yaw angle errors.
Journal Article

Analysis of Vehicle Lateral Dynamics due to Variable Wind Gusts

2014-09-30
2014-01-2449
This study presents a practical theoretical method to judge the aerodynamic response of buses in the early design stage based on both aerodynamic and design parameters. A constant longitudinal velocity 2-DOF vehicle lateral dynamics model is used to investigate the lateral response of a bus under nine different wind gusts excitations. An appropriate 3-D CFD simulation model of the bus shape results is integrated with carefully chosen design parameters data of a real bus chassis and body to obtain vehicle lateral dynamic response to the prescribed excitations. Vehicle model validity is carried out then, the 2-DOF vehicle lateral dynamics model has been executed in MATLAB Simulink environment with the selected data. Simulation represents the vehicle in a straight ahead path then entered a gusting wind section of the track with a fixed steering wheel. Vehicle response includes lateral deviation (LD), lateral acceleration (LA), yaw angle (YA) and yaw rate (YR).
Technical Paper

Interaction of Vehicle Ride Vibration Control with Lateral Stability Using Active Rear Wheel Steering

2009-04-20
2009-01-1042
In this work the effects of vehicle vertical vibrations on the tires/road cornering forces, and then consequently on vehicle lateral dynamics are studied. This is achieved through a ride model and a handling model linked together by a non-linear tire model. The ride model is a half vehicle with four degrees of freedom (bounce and pitch motions for vehicle body and two bounce motions for the two axles). The front and rear suspension are a hydro-pneumatic slow-active systems with 6 Hz cut-off frequency designed based on linear optimal control theory. Vehicle lateral dynamics is modeled as two degrees (yaw and lateral motions) incorporating a driver model. An optimal rear wheel steering control in addition to the front steering is considered in the vehicle model to represent a Four Wheel Steering (4WS) system. The tire non-linearity is represented by the Magic Formula tire model.
Technical Paper

Enhanced Vehicle Lateral Stability in Crosswind by Limited State Kalman Filter Four Wheel Steering System

2007-04-16
2007-01-0841
In this work, a theoretical investigation of four-wheel steering (or shortly 4WS) system is presented using a linear model to simulate vehicle handling characteristics. This model incorporates driver';s operation. The simulation concerns the vehicle in straight running while the vehicle is subjected to side wind excitation. Limitations of measurements in practice are supporting the implementation of limited state feedback systems instead of those which are based on full state feedback information. Therefore, the well known Kalman filter algorithm is used in this work to design a practical 4WS control strategy. This practical system uses only feedback signals of lateral acceleration and front steering angle to obtain the control law. Measurement noise is taken into account and results are generated to obtain the step response of the outputs of interest.
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