Testing Nonlinear Predictive Torque Vectoring on a Scaled Car Driving on a Roadway Simulator

This paper presents an implementation of a nonlinear model predictive control algorithm for autonomous driving applications. The algorithm is based on a two-track nonlinear model of the vehicle that takes into account Kamm’s friction circle and a modified slip definition. It enables all-wheel drive torque vectoring and active front steering. The control algorithm was developed and evaluated through experiments conducted on a scaled four-wheel-drive electric car tested on a treadmill that serves as a roadway simulator. The dSPACE MicroLabBox was used to implement the control algorithm. The algorithm controls both the steering and the torques applied to each wheel based on the desired yaw rate. Its performance was evaluated using double lane change and multiple lane change maneuvers.