Model Based Estimation of Road Friction for Use in Vehicle Control and Safety
The road surface friction is an important characteristic that must be measured accurately to navigate vehicles effectively under different conditions. This parameter is very difficult to estimate correctly as it can take up a value from a broad spectrum of possibilities and the knowledge of this characteristic is of utmost significance in modern day automotive applications. The possible real-time knowledge of friction opens a new range of improvements to the active safety systems such as the Electronic Stability Control (ESC) and Anti-lock Braking Systems (ABS) in addition to providing computerized support to safety applications. The aim of the research is to take an engineering approach to the problem and design a simple and a robust algorithm that can be implemented in any automotive application of choice. After integrating the load transfer model with the four wheel vehicle model, the Dugoff tire models are combined with the aforementioned model to represent the plant model. Using the plant model to design an emulator, the sensor measurements are created and these measurements are then used by a non linear estimator such as the Unscented Kalman Filter to predict the forces at the tires. Friction is then calculated for every iteration and then passed back into the loop.In the end, a comparison of different design methodologies, implementation techniques and performance along with design decisions are discussed so that the current work can be implemented on a real-time controller. In addition to this, a section is dedicated towards highlighting the difference that prior friction information has on the stopping distance of a vehicle. For this purpose, a demonstration is made by creating an ABS control system that uses the predicted friction information and the performance improvement is documented.