Hierarchical drift control of distributed drive electric vehicles integrating terminal sliding mode and RBFNN

Studying drift maneuvers helps improve vehicle handling and broaden the dynamic performance envelope of autonomous vehicles. This paper presents a hierarchical drift control scheme for distributed drive electric vehicles, based on terminal sliding mode control (TSMC). A saturation velocity planner is developed to transform pose errors into bounded reference states, thereby improving stability in drifting trajectory tracking. Subsequently, a TSMC is designed to track these reference states, leveraging its finite-time convergence and robustness. To further improve robustness and adaptability, an radial basis function neural network (RBFNN) approximator is incorporated into the framework for real-time compensation of modeling inaccuracies and unmodeled dynamics. In addition, an optimization-based inverse vehicle dynamics model is employed to map the desired state derivatives to vehicle control inputs. Lastly, co-simulation experiments in Simulink and CarSim demonstrate that the proposed method outperforms baseline control schemes in trajectory tracking and drifting stability.