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Polynomial regressors based data-driven control for autonomous underwater vehicles

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Abstract

This paper proposes a data-driven control approach on the basis of polynomial regressors for autonomous underwater vehicles (AUVs). In contrast to conventional control approach, data-driven control does not require modeling for the systems. It only utilizes the analysis of massive stored dataset to predict the future control input, which can achieve the future output. Generally, the massive stored dataset can be analyzed by short-length vectors linearly. In this paper, a novel point is the improvement of existing data-driven control by polynomial regressors, which improve the control performance of AUVs. By numerical simulations, we illustrate the effectiveness of our proposed approach.

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Acknowledgments

This research was supported by the The National Ntural Science Foundation of China under Grant 61873106, National Nature Science Foundation of Jiangsu Province under Grant BK20171264, Jiangsu Qing Lan Project to Cultivate Middle-aged and Young Science Leaders, Jiangsu Six Talent Peak Project under Grants XYDXX-047, XYDXX-140, University Science Research General Research General Project of Jiangsu Province under Grant 18KJB520005, Lianyungang Hai Yan Plan under Grants 2018-ZD-003, 2018-QD-001, 2018-QD-012, and Natural Science Foundation Project of Huaihai Institute of Technology under Grant Z2017005.

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Authors and Affiliations

  1. Jiangsu Ocean University, Lianyungang, China

    Hongran Li, Weiwei Xu, Heng Zhang & Jian Zhang

  2. Ericsson (China) Communication Company Ltd, Beijing, China

    Yi Liu

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  1. Hongran Li
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  2. Weiwei Xu
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  3. Heng Zhang
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  4. Jian Zhang
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  5. Yi Liu
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Correspondence to Heng Zhang.

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This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge

Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong

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Li, H., Xu, W., Zhang, H. et al. Polynomial regressors based data-driven control for autonomous underwater vehicles. Peer-to-Peer Netw. Appl. 13, 1767–1775 (2020). https://doi.org/10.1007/s12083-020-00878-6

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  • DOI: https://doi.org/10.1007/s12083-020-00878-6

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