Chen, Xuanwei and Yang, Changlin and Cheng, Jiaqi and Hu, Huosheng and Shao, Guifang and Gao, Yunlong and Zhu, Qingyuan (2024) A Novel Iterative Learning-Model Predictive Control Algorithm for Accurate Path Tracking of Articulated Steering Vehicles. IEEE Robotics and Automation Letters, 9 (8). pp. 7373-7380. DOI https://doi.org/10.1109/lra.2024.3422847
Chen, Xuanwei and Yang, Changlin and Cheng, Jiaqi and Hu, Huosheng and Shao, Guifang and Gao, Yunlong and Zhu, Qingyuan (2024) A Novel Iterative Learning-Model Predictive Control Algorithm for Accurate Path Tracking of Articulated Steering Vehicles. IEEE Robotics and Automation Letters, 9 (8). pp. 7373-7380. DOI https://doi.org/10.1109/lra.2024.3422847
Chen, Xuanwei and Yang, Changlin and Cheng, Jiaqi and Hu, Huosheng and Shao, Guifang and Gao, Yunlong and Zhu, Qingyuan (2024) A Novel Iterative Learning-Model Predictive Control Algorithm for Accurate Path Tracking of Articulated Steering Vehicles. IEEE Robotics and Automation Letters, 9 (8). pp. 7373-7380. DOI https://doi.org/10.1109/lra.2024.3422847
Abstract
Refining the path tracking of articulated steering vehicles amidst terrain disturbances presents a formidable challenge. While model predictive control (MPC) offers promise in tackling this issue, its efficacy is often hindered by model intricacies and inaccuracies. In this communication, an innovative approach termed iterative learning-model predictive control (IL-MPC) is introduced to enhance path tracking performance on rugged terrains. Initially, an MPC controller grounded in a simplified kinematic model is established to ensure stability in path tracking. Subsequently, an iterative learning algorithm is integrated to meticulously capture and mitigate MPC controller errors. A comprehensive feedforward-feedback framework coupled with a spatial indexing method is proposed to synergize the strengths of iterative learning and MPC. Through rigorous evaluations across diverse paths and terrains, the method demonstrates robustness against terrain disturbances, affirming its efficacy in real-world scenarios.
Item Type: | Article |
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Uncontrolled Keywords: | Articulated steering vehicle; path tacking; iterative learning; model predictive control; off-road terrain |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 09 Jul 2024 14:30 |
Last Modified: | 19 Jul 2024 10:26 |
URI: | http://repository.essex.ac.uk/id/eprint/38727 |
Available files
Filename: Accepted_Manuscript.pdf