Estimation of Electric Mining Haul Trucks' Mass and Road Slope Using Dual Level Reinforcement Estimator

作者：Ying Zhang, Yingjie Zhang, Zhaoyang Ai, Yun Feng, Jing Zhang, Yi Lu Murphey · 发表于：IEEE Transactions on Vehicular Technology · 年份：2019 · DOI：10.1109/tvt.2019.2943574 · 被引用次数：41 · 研究领域：Vehicle Dynamics and Control Systems、Soil Mechanics and Vehicle Dynamics、Hydraulic and Pneumatic Systems

This paper proposes a dual level reinforcement estimator (DLRE) to estimate the electric mining haul trucks' (EMHTs') total mass and road slope. To design the estimator, the longitudinal dynamics model of EMHT is built and the model's parameters are identified by a recursive least square (RLS) with double forgetting factors. This DLRE consists of an initial level estimation and an extended level estimation. In the initial estimation, the total mass and the road slope are preliminary estimated. In the extended estimation, to prevent the estimated mass error from leading to great errors in the road slope, the road slope is first estimated and then the total mass is estimated based on the estimated road slope. The DLRE overcomes the defects of the traditional recursive-strategy-based estimators whose cumulative estimation errors often lead to poor estimation performance or even lead to estimation divergence. The DLRE performance is validated with an EMHT of 930E, and the experimental results show that the total mass and the road slope are estimated with higher accuracy using the DLRE.