Abstract Model predictive control (MPC) has been extensively utilized in the automotive applications, such as autonomous vehicle path planning and control, hybrid-vehicle energy management, and advanced driver-assistance system design. As a typical model-based control law, MPC relies on a system model to predict the state evolution of the manipulated plant within the prediction horizon. However, a representative yet concise mathematical description of the controlled plant may not always be available in practice. Therefore, model-free strategies, e.g., identification for control and direct data-driven control, have been incorporated into the predictive control framework. Nonetheless, existing model-free predictive controllers usually require reliable datasets and employ complex nonconvex optimizations to identify the underlying system model. Furthermore, their control performances are fundamentally limited by the quality of the training data. Inspired by the model-free control, this paper proposes the ultra-local model predictive control (ULMPC), which is a novel and straightforward model-free predictive control technique with no need for the computationally-extensive model learning process. The proposed ULMPC is implemented for automated vehicle trajectory following. Carsim-Simulink joint simulations and indoor experimental field tests with a scaled car demonstrate its effectiveness.

中文翻译：

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超局部模型预测控制：一种无模型方法及其在自动车辆轨迹跟踪中的应用

摘要 模型预测控制 (MPC) 已广泛应用于汽车应用，例如自动驾驶车辆路径规划和控制、混合动力汽车能量管理和高级驾驶员辅助系统设计。作为典型的基于模型的控制律，MPC 依靠系统模型来预测被操纵设备在预测范围内的状态演变。然而，在实践中可能并不总是可以获得受控设备的代表性但简洁的数学描述。因此，无模型策略，例如控制识别和直接数据驱动控制，已被纳入预测控制框架。尽管如此，现有的无模型预测控制器通常需要可靠的数据集并采用复杂的非凸优化来识别底层系统模型。此外，它们的控制性能从根本上受到训练数据质量的限制。受无模型控制的启发，本文提出了超局部模型预测控制（ULMPC），这是一种新颖且直接的无模型预测控制技术，无需计算量大的模型学习过程。建议的 ULMPC 用于自动跟踪车辆轨迹。Carsim-Simulink 联合仿真和室内实验现场测试证明了其有效性。这是一种新颖且直接的无模型预测控制技术，不需要计算量大的模型学习过程。建议的 ULMPC 用于自动跟踪车辆轨迹。Carsim-Simulink 联合仿真和室内实验现场测试证明了其有效性。这是一种新颖且直接的无模型预测控制技术，不需要计算量大的模型学习过程。建议的 ULMPC 用于自动跟踪车辆轨迹。Carsim-Simulink 联合仿真和室内实验现场测试证明了其有效性。