Search Results

A comprehensive comparison between an air-inflated seat cushion designed for truck seats and a commonly used foam cushion is provided, using a single-axis test rig designed for seat dynamic testing. Different types of tests were conducted in order to evaluate various aspects of each type of cushion; in terms of their response to narrowband (single frequency) dynamics, broadband input of the type that is commonly used in the trucking industry for testing seats, and a step input for assessing the damping characteristics of each cushion. The tests were conducted over a twelve-hour period—in four-hour intervals—measuring the changes that occur at the seat cushion over time and assessing how these changes can affect the metrics that are used for evaluating the cushions. The tests indicated a greater stiffening of the foam cushion over time, as compared with the air-inflated cushion that showed almost no change in stiffness when exposed to a static weight for twelve hours.

Race teams frequently use tools like shock dynamometers (dynos) to characterize the complex behavior of shock absorbers after they are built and before they are put on the race car for testing to make sure they perform as expected. One way to make use of this shock dyno data is to use it to create a model to predict shock absorber performance over a wide range of inputs. These shock models can then be integrated into vehicle simulations to predict how the vehicle will respond to different shock selections, and aid the race engineer to narrow down possible shock setups before track testing. This paper develops an intuitive nonlinear dynamic shock absorber model that can be quickly fit to experimental data and implemented in simulation studies. Unlike other existing dynamic race shock models, it does not suffer from the complexity of modeling complex physical behavior, or the inefficiencies of unstructured black-box modeling.

This study reports the objective results from a project investigating the effectiveness of several newly proposed metrics to compare fatigue performance between two distinct truck seat cushions, specifically standard foam versus air-inflated cushions. The subjective results from this project have shown the drivers in our study prefer the air-inflated seat cushion over their normal foam cushion, and that air-inflated seat cushions provide advantages in terms of comfort, support, and fatigue [1]. This study aims to further explore the differences between these two different seat cushions by highlighting the differences in objective pressure distribution measurements. Road tests were performed using existing commercial trucks in the daily operations of Averitt Express. A retrofit air-inflated seat cushion was installed in the fleet's trucks, and the drivers were allowed to adjust to the seats over approximately one week.

This study reports the subjective results from a project investigating the effectiveness of several newly proposed metrics to compare fatigue performance between two distinct truck seat cushions, specifically standard foam versus air-inflated cushions. We also highlight some of the fundamental differences between air-inflated and foam seat cushion based on driver's perceptions. Road tests were performed using existing commercial trucks in the daily operations of Averitt Express. A retrofit air-inflated seat cushion was installed in the fleet's trucks, and the drivers were allowed to adjust to the seats over approximately one week. After this adjustment period, twelve drivers rode on both the air-inflated seat cushion and their original foam seat cushion during their regularly scheduled routes. Surveys were collected throughout the test sessions and the truck seats were fitted with instrumentation to capture physical measurements of seat pressure distribution.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

Dynamic game theory brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion.

Dynamic “Game Theory” brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In previous studies, it was shown that vehicle stability can be represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the vehicle stability controller (VSC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the VSC command is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion. This study focus on the uncertainty in the inputs, and more specifically, the driver's steering input.

The objective of this paper is to describe a Class 8 heavy truck that the Virginia Tech Center for Transportation Research has modified and instrumented for use in evaluating Intelligent Transportation Systems (ITS) technologies. The truck is capable of recording a variety of data, both electronic and video, in real-time from a suite of sensors and cameras that have been inconspicuously mounted on the tractor. The tractor, trailer, and instrumentation package enable Virginia Tech to conduct commercial vehicle ITS research related to safety and human factors, and advanced vehicle control systems (AVCS). This paper will describe the instrumentation package, and address both general and specific types of research that can be performed using this truck.

A graphical user interface (GUI) toolbox called Vehicle System Simulator (VSS) is developed in MATLAB to ease the use of this vehicle model and hopefully encourage its widespread application in the future. This toolbox uses the inherent MATLAB discrete-time solvers and is mainly based on Level-2 s-function design. This paper describes its built-in vehicle dynamics model based on multibody dynamics approach and nonlinear tire models, and traction/braking control systems including Cruise Control and Differential Braking systems. The built-in dynamics model is validated against CarSim 8 and the simulation results prove its accuracy. This paper illustrates the application of this new MATLAB toolbox called Vehicle System Simulator and discusses its development process, limitations, and future improvements.

In this paper a yaw stability control algorithm along with an emergency roll control strategy have been developed. The yaw stability controller and emergency roll controller were both developed using linear two degree-of-freedom vehicle models. The yaw stability controller is based on Lyapunov stability criteria and uses vehicle lateral acceleration and yaw rate measurements to calculate the corrective yaw moment required to stabilize the vehicle yaw motion. The corrective yaw moment is then applied by means of a differential braking strategy in which one wheel is selected to be braked with appropriate brake torque applied. The emergency roll control strategy is based on a rollover coefficient related to vehicle static stability factor. The emergency roll control strategy utilizes vehicle lateral acceleration measurements to calculate the roll coefficient. If the roll coefficient exceeds some predetermined threshold value the emergency roll control strategy will deploy.