We address the problem of stable and robust control of vehicles with lateral error dynamics for the application of lane keeping. Lane departure is the primary reason for half of the fatalities in road accidents, making the development of stable, adaptive and robust controllers a necessity. Traditional linear feedback controllers achieve satisfactory tracking performance, however, they exhibit unstable behavior when uncertainties are induced into the system. Any disturbance or uncertainty introduced to the steering-angle input can be catastrophic for the vehicle. Therefore, controllers must be developed to actively handle such uncertainties. In this work, we introduce a Neural L1 Adaptive controller (Neural-L1) which learns the uncertainties in the lateral error dynamics of a front-steered Ackermann vehicle and guarantees stability and robustness. Our contributions are threefold: i) We extend the theoretical results for guaranteed stability and robustness of conventional L1 Adaptive controllers to Neural-L1; ii) We implement a Neural-L1 for the lane keeping application which learns uncertainties in the dynamics accurately; iii)We evaluate the performance of Neural-L1 on a physics-based simulator, PyBullet, and conduct extensive real-world experiments with the F1TENTH platform to demonstrate superior reference trajectory tracking performance of Neural-L1 compared to other state-of-the-art controllers, in the presence of uncertainties. Our project page, including supplementary material and videos, can be found at https://mukhe027.github.io/Neural-Adaptive-Control/

翻译：本文针对车道保持应用中车辆横向误差动力学的稳定鲁棒控制问题展开研究。车道偏离是导致半数道路交通事故死亡的主要原因，因此开发稳定、自适应且鲁棒的控制系统至关重要。传统线性反馈控制器虽能实现令人满意的跟踪性能，但当系统引入不确定性时，其会表现出不稳定行为。转向角输入中引入的任何扰动或不确定性都可能对车辆造成灾难性后果。因此，必须开发能够主动处理此类不确定性的控制器。本工作中，我们提出了一种神经L1自适应控制器（Neural-L1），该控制器能够学习前轮转向阿克曼车辆横向误差动力学中的不确定性，并保证系统的稳定性与鲁棒性。我们的贡献主要体现在三个方面：i）将传统L1自适应控制器的稳定性与鲁棒性保证理论结果扩展至Neural-L1；ii）针对车道保持应用实现了能够精确学习动力学不确定性的Neural-L1控制器；iii）在基于物理的仿真器PyBullet上评估Neural-L1性能，并利用F1TENTH平台开展大量实车实验，证明在存在不确定性的情况下，Neural-L1相比其他先进控制器具有更优异的参考轨迹跟踪性能。项目页面（含补充材料与视频）可通过https://mukhe027.github.io/Neural-Adaptive-Control/访问。