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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.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.

The 2014 SAE Buckendale Lecture will address the past developments and challenges of electromechanical “smart” systems for improving commercial vehicles' functionality. Electromechanical systems combine traditional mechanical devices with electrical components to provide far higher degree of functionality and adaptability for improved vehicle performance. The significant advances in microprocessors and their widespread use in consumer products have promoted their implementation in various classes of vehicles, resulting in “smart” devices that can sense their operating environment and command an appropriate action for improved handling, stability, and comfort. The chassis and suspension application of electromechanical devices mostly relate to controllable suspensions and vehicle dynamic management systems, such as Electronic Stability Control.

The mission of the Federal Motor Carrier Safety Administration (FMCSA) is to reduce crashes, injuries, and fatalities involving commercial vehicles [1]. According to the FMCSA, the development, evaluation, and deployment of advanced safety technology will be a key to realizing this goal. Currently, there are many safety systems in development that have the potential to significantly reduce crashes on our nation's roadways. For a variety of reasons, the potential benefits that these systems may provide in reducing crashes may never be realized. The Virginia Tech Transportation Institute (VTTI), in cooperation with FMCSA, has developed a program to evaluate promising safety technologies aimed at commercial vehicle operations (CVO). The objective of FMCSA's Advanced System Testing Utilizing a Data Acquisition System on the Highway (FAST DASH) program is to perform quick turnaround and independent evaluations of promising CVO safety technologies.

Mission demands for U.S. military tactical trucks require them to transport a broad array of cargo types, including intermodal containers. The wide range of mass properties associated with these diverse cargo requirements has resulted in potential for steering stability issues. The potential for steering stability issues largely originates from the high mobility characteristics of single-unit military tactical trucks relative to typical commercial cargo carriers. To quantify the influence of cargo variations on stability, vehicle dynamics experiments were conducted to obtain steering stability measurements for a tactical cargo truck hauling a broad range of rigid cargo loadings. The basic relationship for the understeer gradient measure of directional response behavior and observed data trends from the physical experiments were used to evaluate the relationship between the steering stability of the truck and the mass properties of the cargo.

A long standing problem with heavy vehicle stability has been rollover. With the higher center of gravity, heavier loads, and narrower tracks (as compared to passenger vehicles), they have a lower rollover stability threshold. In this paper, a rollover stability control algorithm based on a two-degrees-of-freedom (DOF) and a three-DOF vehicle model for a two-axle truck was developed. First, the 3DOF model was used to predict the future Lateral load Transfer Rate (LTR). Using this LTR value, the dynamic rollover propensity was estimated. Then, a robust output feedback gain control rollover stability control algorithm based on the combination of active yaw control and active front steering control was developed. A H₂/H∞/poles placement multi-objective control strategy was developed based on the 2DOF reference model.