Handling-Stability Control for Distributed Drive Electric Vehicles via Lyapunov-Based Nonlinear MPC Algorithm

This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem's solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.