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A Hybrid Control Strategy for Light Commercial Vehicle Path Tracking Considering Complex Disturbances
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Jie Hu1, 2, 3, Zhiling Zhang1, 2, 3, Jiefeng Zhong1, 2, 3, Wenlong Zhao1, 2, 3, Jiachen Zheng1, 2, 3, Silong Zhou1, 2, 3, Zijun Qu4
Automotive Engineering | 2024, 46(9) : 1576 - 1586
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Automotive Engineering | 2024, 46(9): 1576-1586
A Hybrid Control Strategy for Light Commercial Vehicle Path Tracking Considering Complex Disturbances
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Jie Hu1, 2, 3, Zhiling Zhang1, 2, 3, Jiefeng Zhong1, 2, 3, Wenlong Zhao1, 2, 3, Jiachen Zheng1, 2, 3, Silong Zhou1, 2, 3, Zijun Qu4
Affiliations
  • 1. Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070
  • 2. Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070
  • 3. Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070
  • 4. Commercial Product R&D Institute,Dongfeng Automobile Co. ,Ltd. ,Wuhan 430000
Published: 2024-09-25 doi: 10.19562/j.chinasae.qcgc.2024.09.005
Outline
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Complex disturbances such as external interference, model uncertainty and parameter perturbation directly affect the accuracy and driving safety of intelligent vehicle path tracking control. Commercial vehicles are more susceptible to complex disturbances during driving because of their load characteristics. A hybrid path tracking control method is proposed in order to improve the accuracy and smoothness of commercial vehicle path tracking. Firstly, a robust sliding mode controller based on extended observer and an incremental LQR controller with stable changes are established. Particle swarm optimization algorithm is used to tune the parameters of the incremental LQR. Then, in order to improve robustness while weakening chattering, a fuzzy controller is used to adjust weight coefficient between them according to vehicle speed and lateral error. Finally, simulation analysis and vehicle experiments are conducted. The experimental data shows that SMC+LQR has good control performance to cope with complex external disturbances.

path tracking  /  complex disturbances  /  sliding mode control  /  extended state observer  /  incremental LQR  /  fuzzy algorithm
Jie Hu, Zhiling Zhang, Jiefeng Zhong, Wenlong Zhao, Jiachen Zheng, Silong Zhou, Zijun Qu. A Hybrid Control Strategy for Light Commercial Vehicle Path Tracking Considering Complex Disturbances[J]. Automotive Engineering, 2024 , 46 (9) : 1576 -1586 . DOI: 10.19562/j.chinasae.qcgc.2024.09.005
Year 2024 volume 46 Issue 9
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.09.005
  • Receive Date:2024-03-16
  • Online Date:2025-07-29
  • Published:2024-09-25
Article Data
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History
  • Received:2024-03-16
  • Revised:2024-04-20
Funding
Affiliations
    1. Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070
    2. Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070
    3. Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070
    4. Commercial Product R&D Institute,Dongfeng Automobile Co. ,Ltd. ,Wuhan 430000
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.09.005
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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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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