AI 中文总结
本文综述了自动驾驶车辆的自适应模型预测控制方法，分析传统模型预测控制的不足，涵盖多种自适应MPC类型，并通过仿真示范其在轨迹跟踪中的权重与速度自适应应用。
AI 中文摘要
本文综述了自动驾驶车辆（AVs）的自适应模型预测控制（AMPC）方法，重点关注可实时动态适应不确定性与变化工况的控制策略，探讨了AMPC在解决自动驾驶车辆控制挑战中的关键作用。本文将AMPC定义为一类模型预测控制（MPC）技术，该技术可基于实时数据修改系统模型、代价函数、约束条件或预测时域。传统MPC虽对约束优化有效，但存在模型精度不足、计算量大、难以应对动态环境的问题，因此亟需AMPC方法。本次综述涵盖了增益调度MPC、在线模型估计MPC、权重自适应MPC、时域自适应MPC、基于学习的MPC，以及将MPC与其他控制方法结合的混合MPC等现有文献。除综述外，本文还展示了自适应MPC控制器的示范仿真，阐明了轨迹跟踪中权重与速度自适应的实际应用要点。
英文摘要
This paper reviews Adaptive Model Predictive Control (AMPC) methods for Autonomous Vehicles (AVs), focusing on control strategies that dynamically adapt to uncertainties and changing conditions in real-time. The critical role of Adaptive Model Predictive Control (AMPC) in addressing the challenges of autonomous vehicle control are discussed. For the scope of this paper, AMPC is defined as a class of Model Predictive Control (MPC) techniques that modify the system model, cost function, constraints, or prediction horizon, based on real-time data. Traditional MPC, while effective for constrained optimization, struggles with model inaccuracies, computational demands, and dynamic environments, necessitating AMPC methods. The review covers existing literature on Gain scheduled MPC, Online Model Estimation MPC, Weight Adaptive MPC, Horizon Adaptive MPC, Learning Based MPC, and Hybrid MPC that combines MPC with other control methods. In addition to the survey, a demonstrative simulation of an adaptive MPC controller is presented that illustrates practical aspects of weight and speed adaptation in trajectory tracking.
Comments16 pages, 4 figures, journal
