用动态主动学习增强高斯过程，实现无人车自适应轨迹跟踪
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles
- 将车辆模型分解为纵横向子系统，在线融合高斯过程补偿误差
- 动态主动学习策略提升采样效率，加速模型收敛
- 在1/10 F1TENTH实车与物理仿真中验证控制性能
本文提出一种基于主动学习的自适应轨迹跟踪控制方法，用于补偿无人地面车辆的建模误差与未建模动态。将车辆的名义模型解耦为横向和纵向子系统，并通过测量数据在线增强高斯过程（GPs）。利用高斯过程的估计均值函数构建反馈补偿器，结合为名义系统设计的LPV状态反馈控制器，形成自适应控制结构。为辅助动态探索，提出一种新型动态主动学习方法，以采集最具信息量的样本，加速训练过程。为进一步评估整体学习工具链所生成控制器的性能，提出一种新颖的迭代式反例算法，用于计算参考轨迹与跟踪误差之间的诱导L2增益。该分析可针对待控系统的多种可能实现进行，提供在车辆动力学变化下的鲁棒性能证明。所提控制方法在高保真物理仿真平台及真实实验中使用1/10比例的F1TENTH电动小车进行了验证。
原文摘要 · Abstract (English)
This article proposes an active-learning-based adaptive trajectory tracking control method for autonomous ground vehicles to compensate for modeling errors and unmodeled dynamics. The nominal vehicle model is decoupled into lateral and longitudinal subsystems, which are augmented with online Gaussian Processes (GPs), using measurement data. The estimated mean functions of the GPs are used to construct a feedback compensator, which, together with an LPV state feedback controller designed for the nominal system, gives the adaptive control structure. To assist exploration of the dynamics, the paper proposes a new, dynamic active learning method to collect the most informative samples to accelerate the training process. To analyze the performance of the overall learning tool-chain provided controller, a novel iterative, counterexample-based algorithm is proposed for calculating the induced L2 gain between the reference trajectory and the tracking error. The analysis can be executed for a set of possible realizations of the to-be-controlled system, giving robust performance certificate of the learning method under variation of the vehicle dynamics. The efficiency of the proposed control approach is shown on a high-fidelity physics simulator and in real experiments using a 1/10 scale F1TENTH electric car.
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