This paper introduces an online physical enhanced residual learning (PERL) framework for Connected Autonomous Vehicles (CAVs) platoon, aimed at addressing the challenges posed by the dynamic and unpredictable nature of traffic environments. The proposed framework synergistically combines a physical model, represented by Model Predictive Control (MPC), with data-driven online Q-learning. The MPC controller, enhanced for centralized CAV platoons, employs vehicle velocity as a control input and focuses on multi-objective cooperative optimization. The learning-based residual controller enriches the MPC with prior knowledge and corrects residuals caused by traffic disturbances. The PERL framework not only retains the interpretability and transparency of physics-based models but also significantly improves computational efficiency and control accuracy in real-world scenarios. The experimental results present that the online Q-learning PERL controller, in comparison to the MPC controller and PERL controller with a neural network, exhibits significantly reduced position and velocity errors. Specifically, the PERL's cumulative absolute position and velocity errors are, on average, 86.73% and 55.28% lower than the MPC's, and 12.82% and 18.83% lower than the neural network-based PERL's, in four tests with different reference trajectories and errors. The results demonstrate our advanced framework's superior accuracy and quick convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.

翻译：本文提出了一种面向网联自动驾驶汽车（CAVs）队列的在线物理增强残差学习（PERL）框架，旨在应对交通环境的动态性和不可预测性带来的挑战。该框架将模型预测控制（MPC）所表征的物理模型与数据驱动的在线Q学习协同结合。针对集中式CAV队列增强后的MPC控制器采用车辆速度作为控制输入，聚焦于多目标协同优化。基于学习的残差控制器为MPC注入先验知识，并校正由交通扰动引起的残差。PERL框架不仅保留了基于物理模型的可解释性与透明性，还在实际场景中显著提升了计算效率与控制精度。实验结果表明：在四种不同参考轨迹与误差条件下的测试中，与MPC控制器及基于神经网络的PERL控制器相比，在线Q学习PERL控制器的位置和速度误差显著降低。具体而言，PERL的累积绝对位置误差和速度误差平均值分别比MPC低86.73%和55.28%，比基于神经网络的PERL低12.82%和18.83%。实验结果展示了本先进框架卓越的精度与快速收敛能力，证明了其在多样化条件下维持队列稳定性的有效性。