提出自适应学习型模型预测控制，实现漂移车辆精准路径追踪。
Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
- 分层架构：上层贝叶斯优化学漂移平衡点，下层模型预测控制执行
- 在路面摩擦参数误标下仍能跟踪克氏线参考路径
- 解决路径追踪与漂移控制的冲突，降低计算负担
漂移车辆控制为极端工况下自动驾驶提供了重要支持，其核心在于沿特定路径跟踪的同时保持车辆状态靠近漂移平衡点（DEP）。然而，传统追踪方法因转向角与偏航率方向相反，难以适配漂移车辆。本文提出一种自适应路径追踪（APT）控制方法，动态调整漂移状态以跟踪参考路径，改进了现有预测路径追踪方法并减轻计算负担。此外，现有策略依赖精确系统模型以计算DEP，但高非线性漂移动力学和敏感车辆参数使该过程更困难。为此，基于APT方法提出自适应学习型模型预测控制（ALMPC）策略，采用上层贝叶斯优化学习DEP与APT控制律，指导下层MPC漂移控制器。该分层架构还可通过分层分离路径追踪与漂移目标，化解固有控制冲突。ALMPC策略在Matlab-Carsim平台验证，仿真结果表明其在路面摩擦参数误标情况下仍能有效控制漂移车辆沿克氏线参考路径行驶。
原文摘要 · Abstract (English)
Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle states near the drift equilibrium points (DEP). However, conventional tracking methods are not adaptable for drift vehicles due to their opposite steering angle and yaw rate. In this paper, we propose an adaptive path tracking (APT) control method to dynamically adjust drift states to follow the reference path, improving the commonly utilized predictive path tracking methods with released computation burden. Furthermore, existing control strategies necessitate a precise system model to calculate the DEP, which can be more intractable due to the highly nonlinear drift dynamics and sensitive vehicle parameters. To tackle this problem, an adaptive learning-based model predictive control (ALMPC) strategy is proposed based on the APT method, where an upper-level Bayesian optimization is employed to learn the DEP and APT control law to instruct a lower-level MPC drift controller. This hierarchical system architecture can also resolve the inherent control conflict between path tracking and drifting by separating these objectives into different layers. The ALMPC strategy is verified on the Matlab-Carsim platform, and simulation results demonstrate its effectiveness in controlling the drift vehicle to follow a clothoid-based reference path even with the misidentified road friction parameter.
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