一种用于辅助车辆漂移的模型预测控制框架
A Model Predictive Control Framework for Assisted Vehicle Drifting
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中文总结 AI 辅助
研究辅助车辆漂移问题，提出基于非线性模型预测控制的框架，通过线控转向和驱动接口解耦驾驶员命令与执行器输入，利用专用激活逻辑，能在简单单轨预测模型下稳定漂移，提升了普通驾驶员的漂移安全性与操作性。
中文摘要 AI 辅助
模型预测控制（MPC）已广泛应用于自动驾驶车辆漂移。辅助漂移（即驾驶员仍参与其中）的研究相对较少。现有方法常依赖如预计算漂移平衡、完全驱动权限或先验路径知识等严格假设，限制了对普通驾驶员的适用性。本文提出一种用于后轮驱动车辆辅助漂移的非线性模型预测控制（NMPC）框架。通过线控转向和线控驱动接口，控制器将驾驶员命令与直接执行器输入解耦，使驾驶员能通过方向盘调节期望侧滑，同时NMPC维持车辆稳定性。专用激活逻辑确保控制器仅在驾驶员有意时启用。高保真模拟表明，即使驾驶员不断改变侧滑参考，该架构使用具有基本轮胎动力学的简单单轨预测模型也能稳定漂移操作。
英文摘要
Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equilibria, full actuation authority, or prior path knowledge, which limit applicability to expert drivers. This paper proposes a nonlinear model predictive control (NMPC) framework for assisted drifting on a rear-wheel-drive vehicle. Through steer-by-wire and drive-by-wire interfaces, the controller decouples driver commands from direct actuator inputs, allowing the driver to regulate the desired sideslip through the steering wheel while the NMPC maintains vehicle stability. A dedicated activation logic ensures that the controller engages only under deliberate driver intent. High-fidelity simulations show that the proposed architecture can stabilize drifting maneuvers using a simple single-track prediction model with basic tire dynamics, even when the sideslip reference is continuously varied by the driver.
