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Model Predictive Anti-disturbance Control for Longitudinal Motion of Intelligent Vehicles Under Multi-source Disturbances
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Zhong Zhang1, 2, Xiaojian Wu1, Huihua Jiang2, Chao Zhang2, Yukang Wan2
Automotive Engineering | 2024, 46(10) : 1816 - 1828
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Automotive Engineering | 2024, 46(10): 1816-1828
Feature Topic: Vehicle Dynamics and Control
Model Predictive Anti-disturbance Control for Longitudinal Motion of Intelligent Vehicles Under Multi-source Disturbances
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Zhong Zhang1, 2, Xiaojian Wu1, Huihua Jiang2, Chao Zhang2, Yukang Wan2
Affiliations
  • 1. School of Advanced Manufacturing,Nanchang University,Nanchang 330031
  • 2. Jiangling Motors Co. ,Ltd. ,Nanchang 330052
Published: 2024-10-25 doi: 10.19562/j.chinasae.qcgc.2024.10.009
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The precision of speed tracking in the longitudinal motion control of intelligent vehicles is affected by multiple sources of disturbances,such as model mismatch and changes in external environments. In this paper,a longitudinal motion anti-disturbance control method that combines disturbance observation and Model Predictive Control (MPC) algorithm is accordingly proposed. Firstly,the relationship between the longitudinal acceleration of the vehicle and various forces is analyzed according to the longitudinal dynamics model of the vehicle,and then it is simplified into a particle motion model with multiple sources of disturbance and a model predictive controller is designed as the upper controller. Secondly,for the internal unmodeled dynamic disturbances and external random disturbances,a linear extended state observer (LESO) is designed to perform real-time estimation and compensation is implemented through a feedforward loop. The closed-loop stability of MPC and the convergence of LESO are analyzed,and finally a model predictive optimal regulation control law of disturbance compensation and state feedback is formed. Furthermore,in order to ensure efficient execution of the control strategy,a first-order anti-disturbance controller is proposed as the lower controller to convert the desired acceleration into engine torque,thereby realizing closed-loop control of the vehicle speed. Finally,the algorithm is deployed on a in-vehicle Microcontroller Unit (MCU) and tested on a real vehicle under multi-speeds and road conditions. The results show that the proposed strategy can quickly and accurately track the target vehicle speed,with excellent anti-disturbance ability.

intelligent vehicle  /  longitudinal speed tracking  /  anti-disturbance control  /  model predictive control  /  extended state observer
Zhong Zhang, Xiaojian Wu, Huihua Jiang, Chao Zhang, Yukang Wan. Model Predictive Anti-disturbance Control for Longitudinal Motion of Intelligent Vehicles Under Multi-source Disturbances[J]. Automotive Engineering, 2024 , 46 (10) : 1816 -1828 . DOI: 10.19562/j.chinasae.qcgc.2024.10.009
Year 2024 volume 46 Issue 10
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.10.009
  • Receive Date:2024-06-23
  • Online Date:2025-07-21
  • Published:2024-10-25
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  • Received:2024-06-23
  • Revised:2024-08-03
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Affiliations
    1. School of Advanced Manufacturing,Nanchang University,Nanchang 330031
    2. Jiangling Motors Co. ,Ltd. ,Nanchang 330052
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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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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