Beyond the stable handling limits: nonlinear model predictive control for highly transient autonomous drifting

Autonomous vehicles that can reliably operate outside the stable handling limits would have access to a wider range of maneuvers in emergencies, improving overall safety. To that end, this paper presents a novel Nonlinear MPC approach for vehicle control with deeply saturated rear tires. Longitudinal slip management is elevated from the chassis control layer into the optimisation problem by using a coupled-slip tire model, and explicitly including wheelspeed dynamics. Terminal costs on sideslip stability help compensate for the finite horizon, while road bounds and static obstacles are encoded using slack constraints. Experiments on a racetrack with a modified Toyota GR Supra validate the controller's ability to smoothly transition from dynamic, non-equilibrium drifting to grip driving. Further experiments demonstrate robustness to significant longitudinal force and wheelspeed disturbances, and showcase the controller flexibly transitioning in and out of the sliding tire regime to balance slack constraints with tracking objectives.能够在稳定操控极限之外可靠运行的自动驾驶汽车，在紧急场景下可拥有更丰富的操控动作选择，进而提升整体行车安全性。为此，本文提出一种面向车辆控制的新型非线性模型预测控制（Nonlinear MPC）方法，该方法适配后轮深度饱和工况。通过采用耦合滑移轮胎模型并显式纳入车轮转速动力学特性，将纵向滑移管控从底盘控制层级提升至优化问题层面。侧滑稳定性的终端代价用于弥补有限时域优化的不足，而道路边界与静态障碍物则通过松弛约束进行编码。基于改装款丰田GR Supra在赛道上开展的实验，验证了该控制器可平稳实现从动态非平衡漂移到抓地行驶的平滑过渡。进一步实验表明，该控制器对显著的纵向力与车轮转速扰动具备鲁棒性，同时展现出灵活切换轮胎滑移工况的能力，以在松弛约束与跟踪目标间取得平衡。