#FSAE TIRE COEFFICIENT OF FRICTION FULL#

This is achieved by applying machine learning techniques to learn a differentiable dynamics model of the system from data. The goal of this paper is to leverage trajectory optimization for performing highly dynamic and complex tasks with robotic systems in absence of an accurate physics simulator. These methods heavily rely on accurate physics simulators, yet some aspects of the physical world, such as friction, can only be captured to a limited extent by most simulators. Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. The chosen topic can possibly lay a foundation for future research into autonomous control where the detailing of decision-making of the controllers will reach the level of multi-physical phenomena concerning the tyre–road interaction. In particular, a non-linear model predictive control (NMPC) based ABS controller with tyre thermal knowledge has been integrated. We evaluate its performance in terms of the resulting braking distance. To this end, a simplified tyre thermal model has been integrated into a model predictive control strategy in order to exploit the thermal dynamics’ dependents within a proposed advanced ABS control system. This work is focused on the evaluation of a potentially improved control strategy’s performance when the control model is fed by instantaneously varying tyre parameters, taking into account the continuously evolving external surface temperature and the vehicle boundary conditions. Indeed, tyre temperature plays a fundamental role in high performance applications due to the dependencies of the cornering stiffness and potential grip in such conditions. Vehicle dynamics can be deeply affected by various tyre operating conditions, including thermodynamic and wear effects.

In addition, the results show that neglecting temperature in the tire model results in higher root mean square error (RMSE) of lateral acceleration and yaw rate.

#FSAE TIRE COEFFICIENT OF FRICTION FULL#

The full vehicle simulation results demonstrated a high correlation to the measurement data for steady-state maneuvers and limited accuracy in highly dynamic driving. A skidpad and full lap maneuvers were chosen to simulate steady-state and transient behavior of the FSAE vehicle.

Considering the model validation, the several maneuvers, typical for FSAE competitions, were performed. The magic formula was modified to incorporate the temperature effect on the force capability of a FSAE tire to achieve higher accuracy in the simulation environment. A thermal tire model was proposed to describe the tread, carcass, and inflation gas temperatures. In the proposed study, the different tires of a FSAE vehicle were tested at a track to parametrize the tire based on the empirical approach commonly known as the magic formula. However, to design different FSAE components, a full vehicle simulation is required including a comprehensive tire model. Commonly, the student team performs a lap simulation as a point mass, bicycle or planar model of vehicle dynamics allow for the design of a top-level concept of the FSAE vehicle. Formula Society of Automotive Engineers (SAE) (FSAE) is a student design competition organized by SAE International (previously known as the Society of Automotive Engineers, SAE).

Fsae tire coefficient of friction