Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error.

中文翻译：

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使用基于系统模型和机器学习方法的拖车质量估计

拖车质量是影响牵引拖车系统稳定性的重要拖车参数之一。在本文中，提出了两种不同的方法来估计任意牵引车-拖车配置的拖车质量；基于动态系统模型和机器学习 (ML) 的方法。分析了基于动态系统模型的估计算法的稳定性，建立估计误差收敛到零。在提出的基于 ML 的方法中，设计了一个深度神经网络 (DNN) 来估计拖车质量。基于 ML 的方法的输入是根据拖拉机-拖车动力学模型选择的，并被认为是通过拖拉机质量、轮胎尺寸和几何形状进行归一化的，因此对于不同的牵引不需要重新训练网络车辆。