Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control
To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking control method based on a dilated convolutional neural network (D...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/10/2588 |
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| author | Lanxin Li Wenhui Pei Qi Zhang |
| author_facet | Lanxin Li Wenhui Pei Qi Zhang |
| author_sort | Lanxin Li |
| collection | DOAJ |
| description | To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking control method based on a dilated convolutional neural network (DCNN). First, an effective dataset was constructed by incorporating historical state information, such as longitudinal tire forces and vehicle speed, to accurately capture vehicle dynamic characteristics and reflect energy variations during motion. Next, a dilated convolutional vehicle system model (DCVSM) was designed by combining vehicle dynamics with data-driven modeling techniques. This model was then integrated into a model predictive control (MPC) framework. By solving a nonlinear optimization problem, a dilated convolutional model predictive controller (DCMPC) was developed to enhance tracking accuracy and reduce energy consumption. Finally, a co-simulation environment based on CarSim and Simulink was used to evaluate the proposed method. Comparative analyses with a traditional MPC and a neural network-based MPC (NNMPC) demonstrated that the DCMPC consistently exhibited superior trajectory tracking performance under various test scenarios. Furthermore, by computing the tire-slip energy loss rate, the proposed method was shown to offer significant advantages in improving energy efficiency. |
| format | Article |
| id | doaj-art-1e513ebea33247bbb03405e669b96261 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-1e513ebea33247bbb03405e669b962612025-08-20T01:56:27ZengMDPI AGEnergies1996-10732025-05-011810258810.3390/en18102588Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive ControlLanxin Li0Wenhui Pei1Qi Zhang2Shandong Key Laboratory of Technologies and Systems for Intelligent Construction Equipment, Shandong Jiaotong University, Jinan 250357, ChinaShandong Key Laboratory of Technologies and Systems for Intelligent Construction Equipment, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaTo address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking control method based on a dilated convolutional neural network (DCNN). First, an effective dataset was constructed by incorporating historical state information, such as longitudinal tire forces and vehicle speed, to accurately capture vehicle dynamic characteristics and reflect energy variations during motion. Next, a dilated convolutional vehicle system model (DCVSM) was designed by combining vehicle dynamics with data-driven modeling techniques. This model was then integrated into a model predictive control (MPC) framework. By solving a nonlinear optimization problem, a dilated convolutional model predictive controller (DCMPC) was developed to enhance tracking accuracy and reduce energy consumption. Finally, a co-simulation environment based on CarSim and Simulink was used to evaluate the proposed method. Comparative analyses with a traditional MPC and a neural network-based MPC (NNMPC) demonstrated that the DCMPC consistently exhibited superior trajectory tracking performance under various test scenarios. Furthermore, by computing the tire-slip energy loss rate, the proposed method was shown to offer significant advantages in improving energy efficiency.https://www.mdpi.com/1996-1073/18/10/2588intelligent drivingdilated convolutional neural networkmodel predictive controltrajectory trackingenergy consumption |
| spellingShingle | Lanxin Li Wenhui Pei Qi Zhang Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control Energies intelligent driving dilated convolutional neural network model predictive control trajectory tracking energy consumption |
| title | Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control |
| title_full | Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control |
| title_fullStr | Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control |
| title_full_unstemmed | Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control |
| title_short | Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control |
| title_sort | research on intelligent vehicle tracking control and energy consumption optimization based on dilated convolutional model predictive control |
| topic | intelligent driving dilated convolutional neural network model predictive control trajectory tracking energy consumption |
| url | https://www.mdpi.com/1996-1073/18/10/2588 |
| work_keys_str_mv | AT lanxinli researchonintelligentvehicletrackingcontrolandenergyconsumptionoptimizationbasedondilatedconvolutionalmodelpredictivecontrol AT wenhuipei researchonintelligentvehicletrackingcontrolandenergyconsumptionoptimizationbasedondilatedconvolutionalmodelpredictivecontrol AT qizhang researchonintelligentvehicletrackingcontrolandenergyconsumptionoptimizationbasedondilatedconvolutionalmodelpredictivecontrol |