用低维残差模型提升自动驾驶MPC的精度与性能
Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model
- 将车辆模型分为固定与可变部分，可变部分用低维残差学习
- 仿真与实车实验显示模型误差显著降低，控制表现更好
- 适合追求高精度控制的自动驾驶系统研发人员
本文提出一种基于学习的模型预测控制（MPC）方法，采用低维残差模型用于自动驾驶。车辆动力学的复杂性是自动驾驶中的关键挑战，影响精确车辆模型的建立，进而制约MPC控制器性能。为此，本文将名义车辆模型分解为不变与可变部分：不变部分通过校准保证精度，可变部分的偏差由低维残差模型学习。残差模型特征选取与名义模型误差最相关的物理变量，并在特征空间中引入物理约束以明确有效区域。该模型与约束被嵌入MPC框架，在仿真与实车实验中验证。结果表明，所提方法显著提升了模型精度与控制器性能。
原文摘要 · Abstract (English)
In this paper, a learning based Model Predictive Control (MPC) using a low dimensional residual model is proposed for autonomous driving. One of the critical challenge in autonomous driving is the complexity of vehicle dynamics, which impedes the formulation of accurate vehicle model. Inaccurate vehicle model can significantly impact the performance of MPC controller. To address this issue, this paper decomposes the nominal vehicle model into invariable and variable elements. The accuracy of invariable component is ensured by calibration, while the deviations in the variable elements are learned by a low-dimensional residual model. The features of residual model are selected as the physical variables most correlated with nominal model errors. Physical constraints among these features are formulated to explicitly define the valid region within the feature space. The formulated model and constraints are incorporated into the MPC framework and validated through both simulation and real vehicle experiments. The results indicate that the proposed method significantly enhances the model accuracy and controller performance.
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