Current status and progress of path tracking control of mining articulated vehicles

BAI Guo-xing; LUO Wei-dong; LIU Li; MENG Yu; GU Qing; LI Kai-lun

doi:10.13374/j.issn2095-9389.2020.07.14.003

Volume 43 Issue 2

Feb. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(2): 193-204

BAI Guo-xing, LUO Wei-dong, LIU Li, MENG Yu, GU Qing, LI Kai-lun. Current status and progress of path tracking control of mining articulated vehicles[J]. Chinese Journal of Engineering, 2021, 43(2): 193-204. doi: 10.13374/j.issn2095-9389.2020.07.14.003

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BAI Guo-xing, LUO Wei-dong, LIU Li, MENG Yu, GU Qing, LI Kai-lun. Current status and progress of path tracking control of mining articulated vehicles[J]. Chinese Journal of Engineering, 2021, 43(2): 193-204. doi: 10.13374/j.issn2095-9389.2020.07.14.003

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Current status and progress of path tracking control of mining articulated vehicles

doi: 10.13374/j.issn2095-9389.2020.07.14.003

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

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Corresponding author: E-mail: myu@ustb.edu.cn
Received Date: 2020-07-14
Publish Date: 2021-02-26

Abstract

Abstract

Path tracking control of articulated vehicles is a focus in the field of mine automation. Mathematical models and path tracking control methods are two key focal points of research in this area. For mathematical models of articulated vehicles, the classic kinematics model without side-slip is suitable as a reference model for low-speed autonomous driving control. However, when this model is used as the reference model, it may lead to an intensification of sliding. In any event, the four-degree-of-freedom dynamic model of articulated vehicles based on the Newton–Euler method meets the requirements of autonomous driving control in principle. However, this model cannot reflect both transient and steady steering characteristics. In the research of path tracking control methods, feedback linearization control, optimal control, sliding mode control, and other control methods without feedforward information cannot effectively solve the problem of a large error when vehicle tracking a reference path with large abrupt changes in curvature. Feedforward–feedback control can be used to solve the above problem, but when the reference path has diverse amplitudes of abrupt changes in curvature, it is necessary to automatically adjust the preview distance. Model predictive control, especially nonlinear model predictive control, can use feedforward information more effectively and does not need to consider the setting of the preview distance. This way, when the articulated vehicle tracks a reference path with large abrupt changes in curvature, accuracy can be effectively improved. Additionally, for the path tracking control of articulated vehicles based on nonlinear model predictive control, three aspects of research need to be deepened. First, for this control method, there is still a trend that the maximum value of the error increases as the reference velocity increases. Second, currently, this control method uses the kinematics model as the prediction model, so it cannot solve the twin problems of reduced accuracy and worsened safety, caused by the lateral velocity when the articulated vehicle runs at a higher reference velocity. Finally, real-time optimization research on this control method is needed.
- mining,
- vehicle,
- articulated,
- unmanned driving,
- path tracking,
- current status,
- progress

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References(76)

References

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