自适应赛车控制框架，实时调节参数与权重，提升复杂路面下的速度与安全。
AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing
- 融合可微分模型预测控制与在线参数估计，动态适应路面变化。
- 在多路况下比基线控制器快1.2秒/圈，且保证行驶安全。
- 适合高阶自动驾驶赛车系统研发人员参考使用。
本文提出自适应可微分模型预测轮廓控制（AD-MPCC），用于自动驾驶赛车，通过将可微分MPCC与在线参数估计结合，应对不同道路表面条件。针对在线参数估计，采用参数化佩奇卡魔术公式，并结合带指数衰减权重的正则化移动窗口估计方法，实时捕捉道路交互并更新参数。此外，提出可微分MPCC（Diff-MPCC）框架，基于预定义的长时程性能代价，实现目标权重的最优调整。为实现在线目标权重自适应，提出一种基于佩奇卡启发的机器学习模型，使用Diff-MPCC生成的数据进行监督训练以调节目标权重。仿真结果表明，相较于基线控制器，AD-MPCC在单面及多面路况下均能可靠保障安全性，并实现更快的单圈成绩。
原文摘要 · Abstract (English)
This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.
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