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Research on Nonlinear Vehicle Model Construction and High-Precision Trajectory Tracking Control Based on Random Forest
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Yaocheng LONG1, Wen SUN1, 2, Zhong ZHANG3, Yong HE1, Guijun LIU1
Chinese Journal of Automotive Engineering | 2025, 15(2) : 197 - 210
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Chinese Journal of Automotive Engineering | 2025, 15(2): 197-210
• System Dynamics Section •
Research on Nonlinear Vehicle Model Construction and High-Precision Trajectory Tracking Control Based on Random Forest
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Yaocheng LONG1, Wen SUN1, 2, Zhong ZHANG3, Yong HE1, Guijun LIU1
Affiliations
  • 1 College of Automotive Engineering，Changzhou Institute of Technology，Changzhou 213032，Jiangsu，China
  • 2 National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130025，China
  • 3 CARTARC（Tianjin）Automotive Engineering Research Institute Co.，Ltd.，Tianjin 300300，China
Published: 2025-03-20 doi: 10.3969/j.issn.2095‒1469.2025.02.08
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In view of the limited accuracy of vehicle dynamics described by traditional control schemes, it is difficult to achieve high precision tracking of the expected state. Therefore, a data-driven model predictive control method for path tracking is introduced. Firstly, a vehicle state parameter observer was constructed using the random forest method. Based on this observer, the nonlinear mapping relationship of vehicle dynamics was analyzed to optimize the controller's underlying mathematical model, thereby reducing the adverse effects of external environmental and mechanical structural disturbances on control performance. Secondly, according to the model predictive control mechanism and vehicle dynamics mapping relationship, the vehicle state space equation was constructed. The linear pattern of vehicle state changes within the local range was analyzed. The quadratic programming cost function for optimizing the steering wheel angle and four-wheel driving force was designed and calculated, aiming to achieve the optimal utilization rate of four-wheel adhesion. Finally, the simulation results show that the proposed control scheme can prevent excessive fluctuations in vehicle body state in the presence of disturbances, and it also maintains a low utilization rate of tire adhesion on undisturbed road sections, achieving safe, stable and high-precision tracking.

machine learning  /  nonlinear mapping  /  high precision tracking control  /  model predictive control  /  autonomous driving
Yaocheng LONG, Wen SUN, Zhong ZHANG, Yong HE, Guijun LIU. Research on Nonlinear Vehicle Model Construction and High-Precision Trajectory Tracking Control Based on Random Forest[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (2) : 197 -210 . DOI: 10.3969/j.issn.2095‒1469.2025.02.08
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.02.08
  • Receive Date：2023-12-28
  • Online Date：2025-07-20
  • Published：2025-03-20
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History
  • Received：2023-12-28
  • Revised：2024-03-10
Funding
Affiliations
    1 College of Automotive Engineering，Changzhou Institute of Technology，Changzhou 213032，Jiangsu，China
    2 National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130025，China
    3 CARTARC（Tianjin）Automotive Engineering Research Institute Co.，Ltd.，Tianjin 300300，China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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Percentage 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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