铰接式自卸卡车的灰箱模型预测控制：基于高斯过程学习的侧偏角

中文摘要 AI 辅助

采矿业对自动化，尤其是铰接式自卸卡车（ADTs）的自主操作，需求日益增长，这引起了人们对精确车辆建模的更多关注。此类模型的重要性在于它们用于模型预测控制（MPC）、基于模型的估计方法以及车辆仿真。虽然动态建模为这些目的提供了可行的解决方案，但它涉及复杂的设置和参数化，并且可能在不断变化的工作环境中需要重新校准。因此，运动学模型在ADT建模中占主导地位，尤其是在MPC中，但代价是预测精度降低。在这项工作中，我们提出了一种使用高斯过程回归（GPR）来学习车辆侧偏角的方法，该侧偏角被确定为导致运动学模型精度降低的主要因素。将学习到的GPR函数增强到运动学模型中，形成一个灰箱模型，旨在缩小与动态模型的差距。我们表明，灰箱模型能够预测侧偏角，从而预测车辆的横向速度，进而提高MPC的预测性能。将所得的灰箱MPC与两个白箱MPC在仿真环境中进行比较。结果表明，最大横向跟踪误差从超过2米改善到0.56米。

英文摘要

The growing demand for automation in the mining industry, particularly for the autonomous operation of articulated dump trucks (ADTs), has drawn increased attention to accurate vehicle modeling. The importance of such models lies in their use in model predictive control (MPC), model-based estimation methods, and vehicle simulation. While dynamic modeling offers a viable solution for these purposes, it is associated with complex setup and parametrization and may require recalibration in changing operating environments. As a result, kinematic models have dominated ADT modeling, especially in MPCs, at the expense of reduced prediction accuracy. In this work, we propose an approach using Gaussian Process Regression (GPR) to learn the sideslip angle of the vehicle, which is identified as the primary contributor to the reduced accuracy of kinematic models. The learned GPR function is augmented into the kinematic model to form a gray-box model that aims to reduce the gap to dynamic models. We show that the gray-box model can predict the sideslip angle and, consequently, the vehicle's lateral velocity, thereby improving the MPC's prediction performance. The resulting gray-box MPC is compared against two white-box MPCs in a simulation environment. The results indicate an improvement in terms of maximum lateral tracking error from over 2 m to 0.56 m.

发表机构

• RWTH Aachen University（亚琛工业大学）

机构由 AI 辅助整理，请以论文原文为准。

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