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This paper proposes a reference steering wheel torque map and a torque tracking algorithm via steer-by-wire to achieve the targeted steering feel. The reference steering wheel torque map is designed using the measurement data of rack force and steering characteristic of a target performance of the vehicle at transition steering test. Since the target performance of the vehicle is only tested in nominal road condition, various road conditions such as disturbances and tire-road friction are not considered. Hence, the measurement data of the rack force that reflects the road conditions in the reference steering wheel torque map have been used. The rack force is the net force which consists of tire aligning moment, road friction force and normal force on the tire kingpin axis. A motor and a magnetorheological damper are used as actuators to generate the desired steering feel using the torque tracking algorithm.

This paper describes a reference steering feel tracking algorithm for Electric-Power-Steering (EPS) system. Development of the EPS system with intended steering feel has been time-consuming procedure, because the feedforward map-based method has been applied to the conventional EPS system. However, in this study, a three-dimensional reference steering feel surface, which is determined from current vehicle states, is proposed. In order to track the proposed reference steering feel surface, sliding mode approach is applied to second-order steering dynamics model considering a coulomb friction model. An adaptive technique is utilized for robustness against uncertainties. In order to validate the proposed EPS control algorithm, hardware-in-the-loop simulation (HILS) has been conducted with respect to a typical steering test. It is shown that the reference steering feel is realized well by the proposed EPS control algorithm.

This paper presents a methodology of correlation between subjective and objective measures of vehicle on-center handling performance. The subjective measure is a professional test driver's rating of vehicle handling, while the objective measure assesses the handling performance via vehicle dynamic responses. Vehicle test data obtained from field testing has been analyzed to investigate links between the objective and subjective measures. Fifty-six physical parameters have been derived from on-centering hysteresis curves. Statistical tools are employed to obtain good correlation between driver rating and physical parameters. Using an interaction formula, a statistical model which relates the driver rating and principal physical parameters has been obtained. The proposed methodology will be used to show the physical parameters influence on subjective assessment and even to predict the subjective assessment of a vehicle handling performance.

This paper proposes a torque tracking algorithm via steer by wire to achieve the target steering feel and proposed a modified friction model to obtain return-ability. A three dimensional reference steering wheel torque map is designed using the measurement data of the steering characteristics of the target vehicle at a transition test and a weave test. In order to track the reference steering wheel torque, a sliding mode control is used in the tracking algorithm. In addition, to achieve return-ability, the modified friction model for steer by wire is used instead of the friction model defined in the reference steering wheel torque map. The modified friction model is composed of various models according to the angular velocity. The angular velocity and the angular acceleration used in the control algorithm are estimated using a kalman filter.

Meeting the growing demand deadlines, reducing the production cost and upgrading the quality control measurements are the reasons why the automotive part manufacturers are venturing into automation. Attaining these objectives is impossible with human inspection for many reasons. Accordingly, the introduction of machine vision system purposely for car seat frame inspection is presented in this study as an alternative for human inspection. This study is designed to meet the demands, features and specifications of car seat frame manufacturing companies in striving for increased throughput of better quality. More specifically, this computer-based inspection system is designed to perform quality measures to detect holes, nuts and welding spots on every car seat frame in real time and ensuring these portions to be intact, precise and in proper place. In this study, the NI Vision Builder software for Automatic Inspection is used as a solution in configuring the aimed quality measurements.

This paper presents an Interacting Multiple Model (IMM) based side slip angle estimation method to estimate side slip angle under various road conditions for vehicle stability control. Knowledge of the side slip angle is essential enhancing vehicle handling and stability. For the estimation of the side slip angles in previous researches, prior knowledge of tire parameters and road conditions have been employed, and sometimes additional sensors have been needed. These prior knowledge and additional sensors, however, necessitates many efforts and make an application of the estimation algorithm difficult. In this paper, side slip angle has been estimated using on-board vehicle sensors such as yaw rate and lateral acceleration sensors. The proposed estimation algorithm integrates the estimates from multiple Kalman filters based on the multiple models with different parameter set.

The growing concern surrounding Sports Utility Vehicle (SUV) rollover incidences and their consequences have prompted to investigate the sensitivity of critical vehicle parameters on rollover. In this paper, dynamic rollover simulation of Sports Utility Vehicles is carried out using a validated nonlinear vehicle model in Matlab/Simulink. A standard model is considered and critical vehicle parameters like CG height, track width and wheel base are varied within chosen specified limits to study its influence on roll behavior during Fishhook steering maneuver. A roll stability criterion based on two wheel lift off phenomenon is adopted for rollover propensity prediction. Further dynamic rollover characteristics of the vehicle are correlated with Static Stability Factor (SSF), Roll Stability Factor (RSF) and Two Wheel Lift off Velocity (TWLV).