A Dual-Level Hierarchical Functional Control Strategy for Four-Wheel Independent Drive Vehicles: Coordination for Enhanced Stability and Safety

With the advancement of electric vehicles towards intelligence and integration, four-wheel independent-drive (FWID) vehicles, characterized by high controllability and structural flexibility, have gained widespread attention. Due to multi-degree-offreedom coupling characteristics, the FWID constitutes complex nonlinear system, necessitating an adaptive control framework to enhance stability and safety. In this paper, a dual-level hierarchical functional control (DHFC) is proposed for FWID, aiming to exploit the potential of the FWID in achieving coordinated optimization of driving safety and stability. The high-level controller is designed to accurately determine the global stability status of FWID by enhancing both parameter estimation accuracy and safety constraints. A reinforcement learning-enhanced high-order cubature Kalman filter (RL-HCKF) improves adaptability and responsiveness in FWID state estimation. Additionally, a hybrid offline-online region of attraction (ROA) identification mechanism is established to delineate safety constraint boundaries for FWID. Meanwhile, the low-level controller adopts stochastic model predictive control (SMPC) to synthesize wheel-level torque vectoring, with dynamically adjusted constraints to enhance the robustness and safety of FWID under uncertain conditions. Simulation evaluations and hardware-in-the-loop (HIL) tests confirm the effectiveness of the proposed strategy. The results demonstrate that, compared to representative existing methods, the DHFC exhibits superior control stability and disturbance adaptability under various driving conditions.