让赛车控制更安全：考虑轮胎摩擦不确定性，避免失控
Risk-Averse Model Predictive Control for Racing in Adverse Conditions
- 用风险规避的MPC框架，显式处理路面摩擦和轮胎参数不确定性
- 在真实车辆上测试，相比传统方法显著降低恶劣路况下失控概率
- 适合自动驾驶赛车、高动态驾驶场景的鲁棒控制研究者
模型预测控制（MPC）在复杂非线性控制任务中易受模型误差影响。尤其在车辆操控极限下，若模型过高估计车辆能力，性能会显著下降。本文提出一种风险规避的MPC框架，显式建模摩擦极限与轮胎参数的不确定性。通过基于样本的条件风险价值（CVaR）约束求解最优控制问题，支持使用多组不同轮胎参数的车辆动力学模型进行规划。该方法结合序列二次规划与GPU并行，实现高效数值求解。在Lexus LC 500上的实验表明，风险规避的MPC能保持稳定性能，而仅依赖单一模型的确定性基线在恶劣道路条件下可能完全失控。
原文摘要 · Abstract (English)
Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle control at the limits of handling suffers when the underlying model overestimates the vehicle's capabilities. In this work, we propose a risk-averse MPC framework that explicitly accounts for uncertainty over friction limits and tire parameters. Our approach leverages a sample-based approximation of an optimal control problem with a conditional value at risk (CVaR) constraint. This sample-based formulation enables planning with a set of expressive vehicle dynamics models using different tire parameters. Moreover, this formulation enables efficient numerical resolution via sequential quadratic programming and GPU parallelization. Experiments on a Lexus LC 500 show that risk-averse MPC unlocks reliable performance, while a deterministic baseline that plans using a single dynamics model may lose control of the vehicle in adverse road conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。