Predictive approach to torque vectoring based on the Koopman operator

Abstract

Torque Vectoring (TV) system uses an individually controlled electric powertrain to improve the dynamic behavior and enhance the handling and stability of a vehicle. In this paper, a Model Predictive Control (MPC) algorithm with a model of the vehicle identified using the Koopman operator theory is proposed. The Koopman operator is a linear predictor for nonlinear dynamical systems based on the lifting of the nonlinear dynamics in a higher-dimensional space where its evolution is linear. Using such a model may allow for achieving similar performance to those of a nonlinear MPC with the computational efficiency of a linear MPC. The Koopman MPC was compared to a Linear Time-Variant (LTV) MPC, a common approach in the existing literature, and showed increased performance.