一种用于车辆规避机动基于学习的模型预测轮廓控制
A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres
AI总结：
本文提出基于学生t过程的L-MPCC算法，通过在线建模模型失配与不确定性，在极限操控下实现更高速度的稳定避障机动，并降低峰值侧偏角。
摘要：
本文提出了一种新颖的基于学习的模型预测轮廓控制（L-MPCC）算法，用于在操控极限下执行规避机动。该算法利用学生t过程（STP）在线最小化模型失配和不确定性。所提出的STP捕获了预测模型与测得的横向轮胎力和横摆角速度之间的失配。这些失配对应于提供给预测模型的后验均值，以提高其准确性。同时，后验协方差沿预测时域传播到车辆横向速度和横摆角速度。STP后验协方差直接依赖于观测数据的方差，因此当在线测量值与训练集中记录的值不同时，其方差更大，反之则更小。因此，这些协方差可用于L-MPCC的代价函数中，以最小化车辆状态不确定性。在高保真仿真环境中，我们证明了所提出的L-MPCC能够成功避开障碍物，在执行双车道变换机动时保持车辆稳定，并且速度高于不带STP的MPCC。此外，与使用高斯过程的L-MPCC相比，所提出的控制器产生了显著更低的峰值侧偏角，从而提高了车辆的机动性。
英文摘要：
This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC's cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle's manoeuvrability compared to an L-MPCC with a Gaussian Process.
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