提出一种考虑纵向运动的鲁棒非线性横向控制框架，提升自动驾驶车辆在变速度下的路径跟踪能力。
Longitudinal-Motion-Aware Lateral Control for Autonomous Vehicles: A Robust Nonlinear Control Framework
- 基于时变纵向速度构建误差模型，融合纵向运动设计控制律
- 仿真与实车测试均显示在变速和参数不确定下仍具高精度与稳定性
- 提供两种可选鲁棒设计，适合对实时性与抗干扰性有不同要求的系统
随着自动驾驶车辆在动态交通环境中运行，横向控制需在纵向速度和加速度变化时进行。然而，现有许多横向控制器依赖恒定速度或工作点假设，在纵向瞬态操作期间性能下降。此外，多数方法假设车辆参数精确已知，而现实中存在参数不确定性。为此，本文提出一种纵向运动感知的鲁棒非线性横向控制框架。首先推导依赖于变化的纵向速度与加速度的跟踪误差模型；利用该模型，通过反馈线性化获得横向误差跟踪的线性输入-输出关系，并将纵向运动嵌入控制律中；随后分析内部动态以保证系统整体稳定性。为应对参数不确定性，提出两种具有不同实现权衡的鲁棒控制设计：(i) 受滑模控制启发的李雅普诺夫重设计（LR）方法，(ii) 增量非线性动态逆（INDI）方法。二者均经严格分析并证明可确保最终有界性，且关键鲁棒性调参参数明确识别。仿真表明，该框架在不同速度与加速度下均实现更优跟踪精度，且对模型不确定性具有鲁棒性，并分析了鲁棒性参数的影响。实车测试进一步验证了其在真实硬件上的实时实现与实际路径跟踪性能。
原文摘要 · Abstract (English)
As autonomous vehicles (AVs) operate in increasingly dynamic traffic conditions, lateral control must be performed while longitudinal speed and acceleration vary. Yet many existing lateral controllers rely on constant-speed or operating-point-based assumptions, which can degrade performance during transient longitudinal maneuvers. Moreover, most methods assume precisely known vehicle parameters, despite real-world parametric uncertainties. To address these limitations, this paper presents a longitudinal-motion-aware robust nonlinear lateral control framework for AVs. It first derives a tracking error model that depends on varying longitudinal speed and acceleration. Using this model, feedback linearization is employed to obtain a linear input-output relation for lateral error tracking while embedding longitudinal motion into the control law. The resulting internal dynamics are then analyzed to ensure overall system stability. To address parameter uncertainty, two robust control designs with distinct implementation trade-offs are proposed: (i) a Lyapunov redesign (LR) approach inspired by sliding mode control, and (ii) an incremental nonlinear dynamic inversion (INDI) method. Both are rigorously analyzed and proven to ensure ultimate boundedness, with key robustness-tuning parameters explicitly identified. Simulations demonstrate enhanced tracking accuracy, consistent performance across varying speeds and accelerations, and robustness to model uncertainties, while also examining the effects of the robustness-related parameters. Real-vehicle tests further confirm real-time implementation and practical path-tracking performance on actual hardware.
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