基于LPV-MPC的横向控制让赛车在160英里/小时下稳定运行。
LPV-MPC for Lateral Control in Full-Scale Autonomous Racing
- 用可变参数模型预测控制，适应高速下的非线性车辆动态
- 实测在71.5米/秒（160英里/小时）下实现稳定控制
- 适用于高算力需求的全尺寸自动驾驶竞速系统
自动驾驶竞速近年来受到广泛关注，但如何在车载系统计算能力限制下选择最优控制器，并满足赛道时间有限、成本高等实际约束仍具挑战。本文提出一种用于横向控制的线性参数可变模型预测控制器（LPV-MPC），部署于IAC AV-24赛车平台，在超过160英里/小时（71.5米/秒）的速度下实现了稳定表现。论文详细阐述了控制器设计、模型参数提取方法及系统级与实现层面的关键考量。此外，还报告了最终比赛运行结果，对车辆动力学与控制器性能进行了全面分析。该框架的Python实现已公开于：https://tinyurl.com/LPV-MPC-acados。
原文摘要 · Abstract (English)
Autonomous racing has attracted significant attention recently, presenting challenges in selecting an optimal controller that operates within the onboard system's computational limits and meets operational constraints such as limited track time and high costs. This paper introduces a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) for lateral control. Implemented on an IAC AV-24, the controller achieved stable performance at speeds exceeding 160 mph (71.5 m/s). We detail the controller design, the methodology for extracting model parameters, and key system-level and implementation considerations. Additionally, we report results from our final race run, providing a comprehensive analysis of both vehicle dynamics and controller performance. A Python implementation of the framework is available at: https://tinyurl.com/LPV-MPC-acados
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