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Predictive Controller Design Using Ant Colony Optimization Algorithm for Unmanned Surface Vessel

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Bio-inspired Computing: Theories and Applications (BIC-TA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 791))

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Abstract

This paper presents a predictive control approach based on ant colony optimization algorithm for critical maneuvering of unmanned surface vehicle in high sea environment. The algorithm uses the generalized predictive control to get the predictive course value. In the process of the algorithm, the ant colony algorithm is used to obtain the optimal control sequence of the rudder angle. The obtained simulation results show that the algorithm solves the problem of overshoot of course controller, and realizes the precise control of USV course in the case of large disturbance of wind and wave, then solves the saturation nonlinear problem of unmanned surface vessel in extreme sea condition.

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Author information

Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Dongming Zhao, Tiantian Yang & Hao Zhou

  2. School of Automation, Huazhong University of Science and Technology, Wuhan, 430070, China

    Wen Ou

Authors
  1. Dongming Zhao
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  2. Tiantian Yang
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  3. Wen Ou
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  4. Hao Zhou
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Corresponding author

Correspondence to Dongming Zhao .

Editor information

Editors and Affiliations

  1. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Cheng He

  2. Automation College, Harbin Engineering University, Harbin, China

    Hongwei Mo

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Automation College, Harbin Engineering University, Harbin, China

    Yuxin Zhao

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© 2017 Springer Nature Singapore Pte Ltd.

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Zhao, D., Yang, T., Ou, W., Zhou, H. (2017). Predictive Controller Design Using Ant Colony Optimization Algorithm for Unmanned Surface Vessel. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_44

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  • DOI: https://doi.org/10.1007/978-981-10-7179-9_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7178-2

  • Online ISBN: 978-981-10-7179-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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