Handling of tire pressure variation in autonomous vehicles: an integrated estimation and control design approach

Abstract

Tire pressure has a high impact on the tire-road contact because it influences the characteristics of the tire forces. During the maneuvering of the vehicle the pressures of the tires may decrease over time, which results in performance degradation or the loss of controllability. This paper proposes a novel integration of tire pressure estimation and path-following control design based on machine learning and Linear Parameter-Varying (LPV) methods. In the estimation process the vehicle dynamic signals, which are available from the conventional on-board sensors, are fused. The values of the estimated tire pressures are incorporated in the LPV control as scheduling variables. The results of the control system are the steering and the differential drive interventions on the vehicle. The effectiveness of the method is illustrated through comprehensive simulation scenarios through the CarMaker simulation enviroment.