Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The results in various road conditions show that the proposed approach outperforms the MPCs with conventional vehicle models.

中文翻译：

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用于自动车辆路径跟踪的学习车辆动力学模型预测控制

模型预测控制器 (MPC) 是一种用于设计自动驾驶汽车 (AV) 路径跟踪控制器 (PTC) 的有效技术。通过采用高保真和准确的车辆模型，可以显着提高 MPC 的性能。该模型应该能够捕获车辆的完整动态，包括非线性和不确定性，而不会给 MPC 带来高计算成本。通过使用车辆操作数据学习车辆动力学来实现数据驱动的方法可以通过在准确的状态预测和 MPC 的计算成本之间提供适当的权衡来提供有前景的解决方案。这项工作提出了一个框架，用于使用现代车辆系统提供的大量数据来设计具有基于神经网络 (NN) 的车辆学习动态模型的 MPC。目标是将比传统车辆模型精度更高的基于神经网络的模型集成到 MPC 中，以提高跟踪性能。所提出的基于 NN 的模型能够高度逼近难以估计的潜在系统状态，并在存在参数不确定性的情况下提供更准确的预测。在各种道路条件下的结果表明，所提出的方法优于具有传统车辆模型的 MPC。并在存在参数不确定性的情况下提供更准确的预测。在各种道路条件下的结果表明，所提出的方法优于具有传统车辆模型的 MPC。并在存在参数不确定性的情况下提供更准确的预测。在各种道路条件下的结果表明，所提出的方法优于具有传统车辆模型的 MPC。