Data-Driven Feedforward Compensation Tuning in Torque Vectoring Control

Abstract

In automotive control, torque vectoring enhances a vehicle dynamical characteristic by independently allocating wheel torques. Torque vectoring algorithms are often based on some form of closed-loop yaw rate tracking, whose reference is generated according to the driver's steering input. Thus, unless the vehicle is equipped with steer-by-wire, from the torque vectoring point of view, the driver's input simultaneously acts as a reference and as a disturbance. This paper presents a method to explicitly consider this aspect in the torque vectoring design by means of a feedforward compensation. At first, we identify the plant model highlighting the effects of the driver input steer on the system. Then, we design the compensation term with H∞ techniques based on the identified model. This solution is then refined using a Data-Driven approach based on a Bayesian optimization algorithm. Results show that the compensation term can reduce the tracking error by 40% and the control effort by 35%.