This work presents a novel Learning Model Predictive Control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in high-speed operational domains. We start from existing LMPC formulations and modify the system dynamics learning method. In particular, our approach uses a nominal, global, nonlinear, physics-based model with a local, linear, data-driven learning of the error dynamics. We conducted experiments in simulation and on 1/10th scale hardware, and deployed the proposed LMPC on a full-scale autonomous race car used in the Indy Autonomous Challenge (IAC) with closed loop experiments at the Putnam Park Road Course in Indiana, USA. The results show that the proposed control policy exhibits improved robustness to parameter tuning and data scarcity. Incremental and safety-aware exploration toward the limit of handling and iterative learning of the vehicle dynamics in high-speed domains is observed both in simulations and experiments.

翻译：本文提出了一种新颖的学习型模型预测控制（LMPC）策略，用于在操控极限状态下进行自主赛车，该策略能够迭代探索和学习高速运行域中的未知动力学特性。我们从现有的LMPC公式出发，改进了系统动力学学习方法。具体而言，我们的方法采用一个标称的全局非线性物理模型，结合局部线性数据驱动的误差动力学学习。我们在仿真和十分之一比例硬件平台上开展了实验，并将所提出的LMPC部署于一款用于Indy自主挑战赛（IAC）的全尺寸自主赛车上，在美国印第安纳州Putnam Park公路赛道上进行了闭环实验。结果表明，所提出的控制策略在参数整定和数据稀疏性方面展现出更强的鲁棒性。仿真和实验均观察到了向操控极限的渐进式安全感知探索，以及在高速域中车辆动力学的迭代学习效果。