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Trajectory Tracking Control Algorithm for Unmanned Mining Transportation Vehicles
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Bo ZHANG1, Bin ZHOU1, Qi XIA1, Nenggen DING1, Yufei DU2, Lujun DONG2, Wei ZHANG2
Chinese Journal of Automotive Engineering | 2024, 14(2) : 168 - 180
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Chinese Journal of Automotive Engineering | 2024, 14(2): 168-180
• Intelligent & Connected Technologies Section/Editor in Chief: GAO Zhenhai •
Trajectory Tracking Control Algorithm for Unmanned Mining Transportation Vehicles
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Bo ZHANG1, Bin ZHOU1, Qi XIA1, Nenggen DING1, Yufei DU2, Lujun DONG2, Wei ZHANG2
Affiliations
  • 1 Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering Beihang University Beijing 100191 China
  • 2 Inner Mongolia Diantou Energy Co., Ltd. Hohhot 010090 China
doi: 10.3969/j.issn.2095–1469.2024.02.02
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The operating environment for unmanned mining transport vehicles is challenging, characterized by unstructured roads such as highcurvature bends and slopes, which demand high requirements for unmanned transportation control. To improve the adaptability of traditional control algorithms like PID and to increase the accuracy of both lateral and longitudinal control in unmanned driving trajectory tracking, this study proposes a combined approach. It involves a multipoint preview lateral control method integrating pure pursuit with PID, and a longitudinal control method considering fuzzy control table parameter fitting. This approach is developed to reduce the number of control parameters while improving the algorithm's effectiveness. Initially, a basic controller is designed using the traditional control algorithm. And then the lateral and longitudinal control algorithms are developed based on the advantages of the basic algorithm. Finally, the performance of these algorithms is verified through hardwareintheloop simulation and onvehicle deployment testing. The experimental results show that compared with the Stanley method, the lateral control algorithm significantly improves vehicle path tracking accuracy. In terms of longitudinal control, the speed tracking error is less than 1 km/h, ensuring the smoothness and comfort of the vehicle's driving performance.

autonomous driving  /  heavy mining cards  /  unstructured roads  /  trajectory tracking  /  control algorithms
Bo ZHANG, Bin ZHOU, Qi XIA, Nenggen DING, Yufei DU, Lujun DONG, Wei ZHANG. Trajectory Tracking Control Algorithm for Unmanned Mining Transportation Vehicles[J]. Chinese Journal of Automotive Engineering, 2024 , 14 (2) : 168 -180 . DOI: 10.3969/j.issn.2095–1469.2024.02.02
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Year 2024 volume 14 Issue 2
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Article Info
doi: 10.3969/j.issn.2095–1469.2024.02.02
  • Receive Date：2023-03-07
  • Online Date：2025-07-21
Article Data
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  • Received：2023-03-07
  • Revised：2023-04-24
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Affiliations
    1 Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering Beihang University Beijing 100191 China
    2 Inner Mongolia Diantou Energy Co., Ltd. Hohhot 010090 China
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https://castjournals.cast.org.cn/joweb/qcgcxb/EN/10.3969/j.issn.2095–1469.2024.02.02
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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