Adaptive trajectory tracking control strategy of intelligent vehicle
The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the v...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720916988 |
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| _version_ | 1850235004986064896 |
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| author | Shuo Zhang Xuan Zhao Guohua Zhu Peilong Shi Yue Hao Lingchen Kong |
| author_facet | Shuo Zhang Xuan Zhao Guohua Zhu Peilong Shi Yue Hao Lingchen Kong |
| author_sort | Shuo Zhang |
| collection | DOAJ |
| description | The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability. |
| format | Article |
| id | doaj-art-0dc2780b42504e9fbcd78f7c187c8df8 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-0dc2780b42504e9fbcd78f7c187c8df82025-08-20T02:02:26ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-05-011610.1177/1550147720916988Adaptive trajectory tracking control strategy of intelligent vehicleShuo ZhangXuan ZhaoGuohua ZhuPeilong ShiYue HaoLingchen KongThe trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.https://doi.org/10.1177/1550147720916988 |
| spellingShingle | Shuo Zhang Xuan Zhao Guohua Zhu Peilong Shi Yue Hao Lingchen Kong Adaptive trajectory tracking control strategy of intelligent vehicle International Journal of Distributed Sensor Networks |
| title | Adaptive trajectory tracking control strategy of intelligent vehicle |
| title_full | Adaptive trajectory tracking control strategy of intelligent vehicle |
| title_fullStr | Adaptive trajectory tracking control strategy of intelligent vehicle |
| title_full_unstemmed | Adaptive trajectory tracking control strategy of intelligent vehicle |
| title_short | Adaptive trajectory tracking control strategy of intelligent vehicle |
| title_sort | adaptive trajectory tracking control strategy of intelligent vehicle |
| url | https://doi.org/10.1177/1550147720916988 |
| work_keys_str_mv | AT shuozhang adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle AT xuanzhao adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle AT guohuazhu adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle AT peilongshi adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle AT yuehao adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle AT lingchenkong adaptivetrajectorytrackingcontrolstrategyofintelligentvehicle |