Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics
Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8886473/ |
| _version_ | 1818621947896922112 |
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| author | Shaohua Wang Yijia Hui Xiaoqiang Sun Dehua Shi |
| author_facet | Shaohua Wang Yijia Hui Xiaoqiang Sun Dehua Shi |
| author_sort | Shaohua Wang |
| collection | DOAJ |
| description | Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively. |
| first_indexed | 2024-12-16T18:17:22Z |
| format | Article |
| id | doaj.art-8642713c8a5843cfa34aa0b107a0d58b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:17:22Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8642713c8a5843cfa34aa0b107a0d58b2022-12-21T22:21:38ZengIEEEIEEE Access2169-35362019-01-01716233316234210.1109/ACCESS.2019.29499928886473Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal DynamicsShaohua Wang0https://orcid.org/0000-0003-4049-5311Yijia Hui1Xiaoqiang Sun2Dehua Shi3School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang, ChinaLongitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively.https://ieeexplore.ieee.org/document/8886473/Intelligent vehiclesintelligent transportation systemlongitudinal dynamics controlsliding mode controlRBF neural network |
| spellingShingle | Shaohua Wang Yijia Hui Xiaoqiang Sun Dehua Shi Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics IEEE Access Intelligent vehicles intelligent transportation system longitudinal dynamics control sliding mode control RBF neural network |
| title | Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics |
| title_full | Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics |
| title_fullStr | Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics |
| title_full_unstemmed | Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics |
| title_short | Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics |
| title_sort | neural network sliding mode control of intelligent vehicle longitudinal dynamics |
| topic | Intelligent vehicles intelligent transportation system longitudinal dynamics control sliding mode control RBF neural network |
| url | https://ieeexplore.ieee.org/document/8886473/ |
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