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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 192–200Cite as

Path Following for Unmanned Surface Vessels Based on Adaptive LOS Guidance and ADRC

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

Abstract

This paper investigates the path following control problem for an unmanned surface vessel (USV). The method proposed is based on an identified model. The course keeping controller using active disturbance rejection control (ADRC) is employed to identify the environmental disturbance and model parameter perturbation. An improved adaptive line-of-sight (ALOS) guidance is proposed to follow reference path. The path can be either straight line or curve. The adaptive control law guarantees the uniformly globally asymptotically stable of the closed-loop USV system. Simulation results show the effectiveness of the proposed control approach.

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References

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Acknowledgement

This work is partially supported by “the Nature Science Foundation of China” (grant NO. 51609033) and “the Fundamental Research Funds for the Central Universities” (grant NO. 3132016315).

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Authors and Affiliations

  1. Dalian Maritime University, Dalian, 116026, China

    Hongyun Huang & Yunsheng Fan

Authors
  1. Hongyun Huang
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  2. Yunsheng Fan
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Corresponding author

Correspondence to Yunsheng Fan .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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© 2017 Springer International Publishing AG

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Huang, H., Fan, Y. (2017). Path Following for Unmanned Surface Vessels Based on Adaptive LOS Guidance and ADRC. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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