Robust Virtual Sensing for Vehicle Agile Manoeuvring: A Tyre-Model-Less Approach

This paper presents a robust virtual sensor to estimate the chassis planar motion states and the tire forces during agile maneuvers using a tire-model-less approach. Specifically, virtual sensing is achieved from standard sensor signals available on the CAN bus of modern vehicles using a modular filter architecture composed of stochastic Kalman filters. A high-fidelity virtual testing environment is constructed in IPG CarMaker using a driver-in-the-loop setup to verify the virtual sensor without compromising driver's safety. Moreover, road random profiles are incorporated into the virtual road to assess the state estimator robustness to high vertical excitation levels. The virtual sensor is simulated under drifting maneuvers performed by an experienced test driver and tested experimentally under Fishhook and Slalom maneuvers. Finally, the state estimator is integrated into a drift controller, and autonomous drift control using exclusively readily available measurements is verified for the first time. As the drift equilibrium depends strongly on the tire-road friction, an adaptive neurofuzzy inference system has been integrated into the virtual sensor structure to provide a continuous approximation of the road friction characteristics (axle lateral force versus slip curve) in rigid and loose surfaces. The findings suggest that it may be possible to develop advanced vehicle controllers without using a tire model. This can lead to a substantial acceleration of development time, particularly in off-road applications, and remove the need for online estimation of tire properties due to pressure, wear, and age.