Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

This paper deals with the torque vectoring control problem for uncertain in-wheel motor vehicles. The main objective of this study is to enhance the cornering performance of vehicles at high speed driving and this is done by generating possibly different motor torques applied to wheels. The proposed controller consists of a model predictive control based outer-loop controller and a disturbance observer based inner-loop controller. The outer-loop controller finds the optimal longitudinal tire force considering state and input constraints. The inner-loop controller estimates the difference between the nominal tire model that is used by the outer-loop controller and the unmodeled disturbance, and compensates the disturbance so that the actual vehicle follows the nominal model. The proposed controller is validated via numerical experiments for circular driving, double lane change, and skidpad scenarios and it is shown that the state tracking performance is improved by using the proposed controller.