This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability inherent to platoon systems. The framework first develops a physics-based controller to model vehicle dynamics, using driving speed as input to optimize safety and efficiency. Then the residual controller, based on neural network (NN) learning, enriches the prior knowledge of the physical model and corrects residuals caused by vehicle dynamics. By integrating the physical model with data-driven online learning, the PERL framework retains the interpretability and transparency of physics-based models and enhances the adaptability and precision of data-driven learning, achieving significant improvements in computational efficiency and control accuracy in dynamic scenarios. Simulation and robot car platform tests demonstrate that PERL significantly outperforms pure physical and learning models, reducing average cumulative absolute position and speed errors by up to 58.5% and 40.1% (physical model) and 58.4% and 47.7% (NN model). The reduced-scale robot car platform tests further validate the adaptive PERL framework's superior accuracy and rapid convergence under dynamic disturbances, reducing position and speed cumulative errors by 72.73% and 99.05% (physical model) and 64.71% and 72.58% (NN model). PERL enhances platoon control performance through online parameter updates when external disturbances are detected. Results demonstrate the advanced framework's exceptional accuracy and rapid convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.

翻译：本文提出一种用于网联自动驾驶车辆编队控制的物理增强残差学习框架，以应对编队系统固有的动态性与不可预测性。该框架首先构建基于物理的控制器来建模车辆动力学，以行驶速度作为输入来优化安全性与效率；随后通过基于神经网络学习的残差控制器，增强物理模型的先验知识并修正车辆动力学引起的残差。通过将物理模型与数据驱动的在线学习相结合，PERL框架既保持了基于物理模型的可解释性与透明度，又提升了数据驱动学习的适应性与精确度，在动态场景中实现了计算效率与控制精度的显著提升。仿真与机器人车辆平台测试表明，PERL框架显著优于纯物理模型与纯学习模型，将平均累积绝对位置误差与速度误差分别降低达58.5%与40.1%（相较于物理模型）以及58.4%与47.7%（相较于神经网络模型）。缩比机器人车辆平台测试进一步验证了自适应PERL框架在动态扰动下具有更优的精度与快速收敛性，将位置与速度累积误差分别降低72.73%与99.05%（相较于物理模型）以及64.71%与72.58%（相较于神经网络模型）。当检测到外部扰动时，PERL通过在线参数更新来提升编队控制性能。实验结果证明了该先进框架具有卓越的精度与快速收敛能力，验证了其在多种条件下维持编队稳定性的有效性。