提出新型非线性转向模型，实测在闭环控制中表现最佳。
Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
- 用多项式曲面拟合构建最小参数化的新型经验模型
- 闭环测试中该模型实现最优路径追踪与最快圈速
- 强调需在完整控制链路中评估前馈策略
前馈转向控制是自主赛车分层控制架构的关键组件，旨在通过预测车辆逆横向动力学减少反馈控制器的修正量。本文系统对比了两种基于学习的方法与两种经验（解析）方法的前馈转向控制器。我们提出一种基于多项式曲面拟合的新{ehd}公式，以最小参数化方式捕捉速度相关的非线性转向特性。在基于阿布扎比自动驾驶赛车联盟真实比赛的高保真仿真框架中测试，采用高保真双轨车辆动力学模拟器。开环评估显示，基于学习的控制器预测误差最低；然而闭环测试表明，其更高精度并未转化为更优的路径跟踪性能或圈速，即使经过迭代微调也未改善。相反，所提出的EHD方法在闭环鲁棒性和圈速方面表现最佳，凸显了在完整轨迹规划与控制软件栈内评估前馈策略的重要性。代码开源：https://github.com/TUMRT/steering_ff_control。
原文摘要 · Abstract (English)
Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controllers by predicting the vehicle's inverse lateral dynamics. This paper presents a systematic benchmark of two learning-based and two empirical (analytical) feedforward steering controllers. We introduce a new \acf{ehd} formulation based on a polynomial surface fit that captures velocity-dependent nonlinear steering behavior with minimal parametrization. We test the feedforward controllers in a high-fidelity simulation framework based on the real-world Abu Dhabi Autonomous Racing League competition, using a high-fidelity double-track vehicle dynamics simulator. Open-loop evaluation shows that the learning-based controllers achieve the lowest prediction errors; however, closed-loop testing reveals that this improved accuracy does not translate into superior path tracking performance or lap times, even after iterative fine-tuning. In contrast, the proposed EHD approach achieves the best overall closed-loop robustness and lap time, highlighting the necessity of evaluating feedforward strategies within the complete trajectory planning and control software stack. Our code is available at https://github.com/TUMRT/steering_ff_control.
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