Robust Dynamic Control for Electric Vehicles With Estimation of Parametric Uncertainties and External Disturbances

This study addresses the control problem in Electric Vehicles (EVs), specifically focusing on designing a robust dynamic control system capable of estimating parametric uncertainties and external disturbances. The EV under consideration is equipped with an Active Front Steering (AFS) system, which introduces an incremental steering angle to the front wheels. Additionally, the vehicle significantly enhances the powertrain, employing Permanent Magnet Synchronous Motors (PMSMs) mounted on the left and right wheel axle shafts. This configuration not only provides longitudinal traction but also facilitates appropriate yaw torque control, similar to the functionality of a Rear Torque Vectoring (RTV) system. This paper demonstrates how integrating the AFS system and RTV within the framework of integral control ensures that the vehicle can accurately track a predetermined trajectory, even in the presence of environmental disturbances, parametric uncertainties, or unmodeled dynamics. However, the proposed control strategy relies on precise information from the EV, particularly lateral velocity, which is challenging to measure directly in practical applications. To address this issue, a robust nonlinear observer is proposed to reconstruct the lateral velocity. Furthermore, High-Order Sliding Mode (HOSM) estimators are employed to approximate the parametric uncertainties and external perturbations in finite-time. The controller's performance is evaluated through simulations of a demanding double-step steer maneuver, conducted using Simulink software.