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A Tuner of Trajectory Control Parameters and the Construction Method of its Training Set
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Kegang Zhao, Weilin Ou, Zheng Zhang, Zhihao Liang
Automotive Engineering | 2025, 47(2) : 248 - 258
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Automotive Engineering | 2025, 47(2): 248-258
A Tuner of Trajectory Control Parameters and the Construction Method of its Training Set
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Kegang Zhao, Weilin Ou, Zheng Zhang, Zhihao Liang
Affiliations
  • School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641
Published: 2025-02-25 doi: 10.19562/j.chinasae.qcgc.2025.02.005
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To improve the control accuracy of intelligent vehicle tracking controllers in variable operating conditions, controllers generally use multidimensional control parameter tables based on operating condition characteristics. When engineers manually adjust multidimensional control parameter tables, the workload is large and the tuning effect is not satisfactory. In order to enable the tracking controller of dynamic parameter adjustment capability, in this paper a vehicle speed and curvature adaptive parameter tuner is proposed based on radial basis function (RBF) neural network. Besides, a training set construction method based on Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO) algorithm is proposed to address the problems of excessive real vehicle testing interactions and heavy tuning workload encountered during the training of tuner. By grouping typical operating conditions based on vehicle speed in the construction process of the training set, all different curvature working conditions within each vehicle speed working condition group are trained using the dynamic model trained on the data collected from tracking the straight-line scene at that vehicle speed for parameter tuning. By sharing the model, the number of real vehicle interactions is reduced. Real vehicle experiments show that the parameter adaptive tracking controller proposed in this paper has better lateral trajectory-tracking performance compared to controllers with fixed parameters under medium and low speed conditions.

trajectory tracking control  /  RBF neural network  /  multidimensional control parameters  /  training set construction  /  MC-PILCO
Kegang Zhao, Weilin Ou, Zheng Zhang, Zhihao Liang. A Tuner of Trajectory Control Parameters and the Construction Method of its Training Set[J]. Automotive Engineering, 2025 , 47 (2) : 248 -258 . DOI: 10.19562/j.chinasae.qcgc.2025.02.005
Year 2025 volume 47 Issue 2
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Article Info
doi: 10.19562/j.chinasae.qcgc.2025.02.005
  • Receive Date:2024-07-24
  • Online Date:2025-07-09
  • Published:2025-02-25
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  • Received:2024-07-24
  • Revised:2024-09-02
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    School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2025.02.005
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表12种不同金属材料的力学参数

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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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