Adaptive MPC‐based path tracking integrated control for distributed drive electric vehicles with rollover prevention under steering failure

Abstract In path tracking control, the focus is typically on tracking accuracy, often neglecting the coupling effects of vehicle dynamics, parameter adaptability, and special scenarios such as steering actuator failure, resulting in poor control performance and insufficient adaptability under complex conditions. To address this issue, this paper proposes an integrated path tracking control method for distributed drive electric vehicles considering rollover prevention based on adaptive model predictive control (MPC) algorithm under steering motor complete failure. Firstly, considering the dependence of the designed controller on the vehicle state parameters, a robust unknown input observer is designed to estimate the vehicle front wheel angle and roll angle. Then, by combining the estimated angle values obtained online, the influence analysis of vertical load transfer and the tire cornering stiffness can be constructed. Afterwards, an adaptive MPC path tracking controller considering the coupling effect of lateral motion and roll motion is established to obtain the desired front wheel angle and additional yaw moment. Meanwhile, a sliding mode control (SMC) based differential steering controller is established to track the desired front wheel angle. Specially, for differential steering capability and vehicle yaw stability, an improved torque distribution approach based on Karush–Kuhn–Tucker (KKT) condition is proposed to allocate the required torque to the four wheels. Finally, a CarSim‐Simulink co‐simulation and a hardware‐in‐the‐loop experiment are performed, whose results demonstrate that the proposed scheme can significantly improve tracking accuracy and vehicle stability under complete steering motor failure compared with the traditional MPC method.