Meta-Learning Enhanced Online Adaptive Control for Robust Motion of Autonomous Electric Vehicles

Motion control of autonomous electric vehicles (AEVs) faces severe challenges due to significant uncertainties introduced by dynamic environments, which may lead to potential safety issues. To address this problem, this paper proposes a meta-learning-enhanced online adaptive control method for AEVs to realize robust and high-precision motion control. First, a novel Meta-Learning-based Online Adaptive (MLOA) modeling approach is introduced, which enables rapid online adaptation of vehicle dynamics through few-shot learning combined with real-time operational data. This approach effectively captures dynamic behaviors in previously unseen tasks. Furthermore, the MLOA model is integrated into a Stochastic Model Predictive Control to enhance control adaptability and responsiveness under various conditions. Meanwhile, chance constraints are incorporated to handle random disturbances, thereby strengthening the robustness of the control strategy. The proposed method is validated through both simulations and real-vehicle experiments. Results show that the controller adapts within 1.8 s in previously unseen tasks and achieves up to 72.6% reduction in lateral tracking errors compared to the baseline method, and maintains an average computation time of only 0.0148 s per control step. These findings confirm the proposed method’s ability to maintain high trajectory tracking accuracy, fast response, and real-time feasibility under uncertainties and highlight the effectiveness of combining meta-learning with optimal control, providing a robust and adaptive control framework for autonomous driving in diverse and complex environments.