A New Fuzzy Based Stability Index Using Predictive Vehicle Modeling and GPS Data

The use of global positioning systems, or GPS, as a means of logistical organization for fleet vehicles has become more widespread in recent years. The system has the ability to track vehicle location, report on diagnostic trouble codes, and keep tabs on maintenance schedules. This helps to improve the safety and productivity of the vehicles and their operators. Additionally, the increasing use of yaw and roll stability control in commercial trucks has contributed to an increased level of safety for truck drivers. However, these systems require the vehicle to begin a yaw or roll event before they assist in maintaining control. This thesis presents a new method for utilizing the GPS signal in conjunction with a new fuzzy logic-based stability index, the Total Safety Margin (TSM), to create a superior active safety system.

This thesis consists of four main components: An overview of GPS technology is presented with coverage of several automotive-based applications. The proposed implementation of GPS in the new Hardware-in-the-Loop (HIL) driving simulator under development at the Virginia Tech Center for Vehicle Systems and Safety (CVeSS) is presented.

The three degree-of-freedom (3DOF), linear, single track equation set used in the Matlab simulations is derived from first principles.

Matlab and TruckSim 7® simulations are performed for five vehicle masses and three forward velocities in a ramp-steer maneuver. Using fuzzy logic to develop the control rules for the Total Safety Margin (TSM), TSM matrices are built for both the Matlab and TruckSim 7® results based on these testing conditions. By comparing these TSM matrices it is shown that the two simulation methods yield similar results.

A discussion of the development and implementation of the aforementioned HIL driving simulator is presented, specifically the steering subsystem. Using Matlab/Simulink, dSPACE ControlDesk, and CarSim RT® software it is shown that the steering module is capable of steering the CarSim RT® simulation vehicle accurately within the physical range of the steering sensor used.