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.

翻译：本文提出了一种基于低维残差模型的学习型模型预测控制方法用于自动驾驶。自动驾驶面临的关键挑战之一是车辆动力学的复杂性，这阻碍了精确车辆模型的建立。不准确的车辆模型会严重影响MPC控制器的性能。为解决此问题，本文将名义车辆模型分解为不变部分与可变部分。不变部分的精度通过标定保证，而可变部分的偏差则通过一个低维残差模型进行学习。残差模型的特征被选为与名义模型误差最相关的物理变量。这些特征之间的物理约束被明确构建，以定义特征空间内的有效区域。所构建的模型与约束被整合到MPC框架中，并通过仿真与实车实验进行了验证。结果表明，所提方法显著提升了模型精度与控制器性能。