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Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019) pp 451–459Cite as

Fuzzy RBF Neural Network Control for Unmanned Surface Vehicle

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1088))

Abstract

For the problem of the heading control of USV, the intelligent control method is achieved by fuzzy RBF neural network. Considering the uncertainty of the USV motion system, the fuzzy system with universal approximation performance is used to fuzzily approximate the uncertainties and external disturbances in the USV motion model. To further enhance the fuzzy system approximation, fuzzy rules were optimized online by RBF neural network with fast learning ability. The intelligent control method proposed realizes continuous and stable tracking of USV heading through simulation.

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References

  1. Sun, Jianming, and Renqaing Wang. 2017. Application of genetic algorithm and neural network in ship’s heading PID tracking control. Advances in Intelligent Systems and Computing 686: 27–35.

    Google Scholar 

  2. Ning, Wang, Lv Shuailin, and Liu Zhongzhong. 2016. Global finite-time heading control of surface vehicles. Neurocomputing 106: 662–666.

    Google Scholar 

  3. Wang, Aihui, Qiang Zhang, and Dongyun Wang. 2015. Robust tracking control system design for a nonlinear IPMC using neural network-based sliding mode approach. International Journal of Advanced Mechatronic Systems 6: 269–276.

    Article  Google Scholar 

  4. Benslimane, Hocine, Abdesselem Boulkroune, and Hachemi Chekireb. 2018. Adaptive iterative learning control of nonlinearly parameterised strict feedback systems with input saturation. International Journal of Automation and Control 12: 251–270.

    Article  Google Scholar 

  5. Wang, Yang, Chen Guo, and Fuchun Sun. 2015. Dynamic neural fuzzified adaptive control of ship course with parametric modelling uncertainties. International Journal of Modelling, Identification and Control, 4: 251–258.

    Article  Google Scholar 

  6. El Yakine, Nour, Mohamed Menaa Kouba, Mourad Hasni, and Mohamed Boudour. 2016. Design of intelligent load frequency control strategy using optimal fuzzy-PID controller. International Journal of Process Systems Engineering 4: 41–64.

    Article  Google Scholar 

  7. Renqiang, Wang, Zhao Yuelin, and Sun Jianming. 2016. Application of optimized RBF neural network in ship’s autopilot design. Proceedings of Advanced Information Management, Communicates, Electronic and Automation Control Conference, 1642–1646.

    Google Scholar 

  8. FAN Yunsheng, SUN Xiaojie, WANG Guofeng, GUO Chen. On Fuzzy Self-adaptive PID Control for USV Course, Proceedings of the 34th Chinese Control Conference, 7: 8472–8478 (2015).

    Google Scholar 

  9. Wang, Renqiang, Yuelin Zhao, and Keyin Miao. 2016. Application of neural network minimum parameter learning algorithm in ship’s heading tracking control. Proceedings of International Symposium on Computational Intelligence and Design, 135–139.

    Google Scholar 

  10. Deng, Hua, Renqiang Wang, Jingdong Li, etc. 2018. RBF neural network control for USV with input saturation, MATEC Web of Conferences 214, 1–5.

    Article  Google Scholar 

  11. Wang, Renqiang, Hua Deng, Keyin Miao, Yue Zhao, and Du Jiabao. 2017. RBF network based integral backstepping sliding mode control for USV. Matec Web of Conferences 139: 143–146.

    Article  Google Scholar 

  12. Wang, Renqiang, and Yuelin Zhao. 2016. Keyin Miao application of neural network minimum parameter learning algorithm in ship’s heading tracking control. International Symposium on Computational Intelligence and Design 8: 135–139.

    Google Scholar 

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Acknowledgements

This research was supported by Natural Science Research Project of Jiangsu Province (Grant No. 18KJB580003), and Innovation Fund of Science and Technology of Jiangsu Maritime institute (Grant No. KJCX1811).

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Authors and Affiliations

  1. School of Navigation, Jiangsu Maritime Institute, Nanjing, China

    Renqiang Wang, Keyin Miao, Jianming Sun, Hua Deng & Yue Zhao

Authors
  1. Renqiang Wang
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  2. Keyin Miao
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  3. Jianming Sun
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  4. Hua Deng
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  5. Yue Zhao
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Corresponding author

Correspondence to Renqiang Wang .

Editor information

Editors and Affiliations

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

    Chuanchao Huang

  2. Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Wei Chan

  3. University of Aizu, Fukushima, Japan

    Neil Yen

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© 2020 Springer Nature Singapore Pte Ltd.

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Cite this paper

Wang, R., Miao, K., Sun, J., Deng, H., Zhao, Y. (2020). Fuzzy RBF Neural Network Control for Unmanned Surface Vehicle. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_56

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