Trajectory-Tracking for Mobile Vehicle Based on Iterative Learning Control

Trajectory tracking is currently one of the hot topics in the automotive field and has significant importance. Trajectory tracking mainly refers to the consistency between the path traveled within a specified time and a predetermined reference path. It is necessary to ensure that the mobile vehicle with no collisions and less time consumption during this time. Trajectory tracking has difficulties modeling and slowly algorithm. In view of the above situation, this article designs a disturbance PD type Iterative Learning Control (ILC) algorithm for trajectory tracking. When the mobile vehicle encounters obstacles or disturbances by the trajectory, it also can quickly adjust its planned by the system stability.

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Published
2024-12-27
DOI
https://doi.org/10.1109/ic2ecs64405.2024.10928317
Primary Topic
Iterative Learning Control Systems
Type
article
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Trajectory-Tracking for Mobile Vehicle Based on Iterative Learning Control

Dongjie Tan, Zhenxian Zheng, Tengyue Yan, Bohan Xu et al.
Iterative Learning Control Systems
article

Trajectory-Tracking for Mobile Vehicle Based on Iterative Learning Control

Dongjie Tan, Zhenxian Zheng, Tengyue Yan, Bohan Xu, Xiaoxiao Du, Lu Song, Hao Ding, Liyan Li, Yueheng Li, Jinzhuang Dou
article en

Abstract

Trajectory tracking is currently one of the hot topics in the automotive field and has significant importance. Trajectory tracking mainly refers to the consistency between the path traveled within a specified time and a predetermined reference path. It is necessary to ensure that the mobile vehicle with no collisions and less time consumption during this time. Trajectory tracking has difficulties modeling and slowly algorithm. In view of the above situation, this article designs a disturbance PD type Iterative Learning Control (ILC) algorithm for trajectory tracking. When the mobile vehicle encounters obstacles or disturbances by the trajectory, it also can quickly adjust its planned by the system stability.

Chinese Academy of Sciences (CN), Institute of Automation (CN)
Openalex Percentile: Top 64%
Iterative Learning Control Systems
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