All-Wheel Torque Vectoring for Driver-in-the-Loop Drifting

Abstract

Drifting, a specialized form of sideslip control, involves intentionally inducing and maintaining a state of oversteer for lateral sliding of the vehicle. While previous research has primarily focused on autonomous drift control, the integration of the driver in the control loop remains largely unexplored. This thesis aims to explore how a vehicle sideslip control system can be designed and evaluated for driver-involved drifting scenarios. To this end, a seven degree-of-freedom vehicle model and a tire model calibrated on data from a test vehicle were used to develop a model-based optimization scheme, leveraging the capabilities of all-wheel torque vectoring to achieve the desired drifting behavior. Phase portrait and real data analyses during drifting maneuvers were conducted to understand drift dynamics and develop the reference generator, which calculates targets for the controller. The reference generator and controller were validated through simulations using IPG CarMaker, drifting along a constant radius circle and a section of a track. The results demonstrate that the developed control system enables stable and controllable drift behavior with a driver-in-the-loop and allows drivers to fully exploit their vehicle’s capabilities.