AI 中文总结
提出AD-MPCC框架，结合可微分MPCC与在线参数估计，通过Pacejka魔术公式和正则化移动视界估计适应路面变化，并利用可微分MPCC优化目标权重，实现更快圈速和安全性。
AI 中文摘要
本文提出自适应可微分模型预测轮廓控制（AD-MPCC），一种用于自主赛车的框架，它将可微分MPCC与在线参数估计相结合，以处理变化的路面条件。对于在线参数估计，我们利用参数化的Pacejka魔术公式以及具有指数衰减权重的正则化移动视界估计方案，来捕捉道路相互作用并实时更新参数。此外，我们提出了一个可微分MPCC（Diff-MPCC）框架，该框架能够基于预定义的长期性能成本对目标权重进行最优调整。为了实现用于在线目标权重自适应的Diff-MPCC，我们提出了一个基于Pacejka的机器学习模型，该模型使用Diff-MPCC生成的数据进行监督训练，以调整目标权重。仿真结果表明，与基线控制器相比，AD-MPCC在单表面和多表面场景中均能可靠地确保安全性并实现更快的圈速。
英文摘要
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.
