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Robust Adaptive Neural Fault-Tolerant Control of Hypersonic Flight Vehicle

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Cognitive Systems and Signal Processing (ICCSIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

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Abstract

This paper proposes a fault-tolerant back-stepping control method for the hypersonic flight vehicle. An adaptive law is proposed to make sure that the normal actuators could compensate the ineffective actuators while failure exists. Meanwhile, the RBF NN is employed to estimate the unknown nonlinearity of the model. The simulation results verify the effectiveness of the proposed approach.

This work was supported by National Natural Science Foundation of China (61622308), Aeronautical Science Foundation of China (2015ZA53003), Natural Science Basic Research Plan in Shaanxi Province (2016KJXX-86), Fundamental Research Funds of Shenzhen Science and Technology Project (JCYJ20160229172341417).

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Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Yuyan Guo, Qiang Wang & Bin Xu

Authors
  1. Yuyan Guo
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  2. Qiang Wang
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  3. Bin Xu
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Corresponding author

Correspondence to Bin Xu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Dewen Hu

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Guo, Y., Wang, Q., Xu, B. (2017). Robust Adaptive Neural Fault-Tolerant Control of Hypersonic Flight Vehicle. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_5

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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