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Neural-networks-based Adaptive Control for an Uncertain Nonlinear System with Asymptotic Stability

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Abstract

This paper proposes a neural-networks(NN)-based adaptive controller for an uncertain nonlinear system with asymptotic stability. While the satisfactory performance of the NN-based adaptive controller is validated well in various uncertain nonlinear systems, the stability is commonly restricted to the uniformly ultimate boundedness(UUB). To improve the UUB of the NN-based adaptive control to the asymptotically stability(AS) with continuous control, the existing NN-based adaptive controller is augmented with a robust-integral-signum-error (RISE) feedback term, and overall closed-loop stability is rigorously analyzed by modifying the typical stability analysis for the RISE feedback control. To demonstrate the effectiveness of the proposed controller, numerical simulations for a fault tolerant flight control with a nonlinear F-16 aircraft model are performed.

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Authors and Affiliations

  1. 5th R&D Institute 2nd Directorate at Agency for Defense Development, Daejeon, Korea

    Jongho Shin

  2. Department of Aerospace Engineering, Chungnam National University, 99 Daehakro, Yuseong-gu, Daejeon, 34134, Korea

    Seungkeun Kim

  3. School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire, MK430AL, UK

    Antonios Tsourdos

Authors
  1. Jongho Shin
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  2. Seungkeun Kim
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  3. Antonios Tsourdos
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Corresponding author

Correspondence to Seungkeun Kim.

Additional information

Recommended by Associate Editor Hongyi Li under the direction of Editor Myo Taeg Lim. This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD130070ID).

Jongho Shin received the B.S. degree in Mechanical Engineering from Soongsil University, Seoul, Korea, in 2005, and then acquired the Ph.D. degree in the Department of Mechanical and Aerospace Engineering from Seoul National University in 2011. He is currently a senior researcher in Agency for Defense Development, Daejeon, Korea. His research interests include adaptive/robust/optimal control with applications to aerial robots, autonomous ground robots, surface vessel and other mechanical systems.

Seungkeun Kim received the B.Sc. degree in mechanical and aerospace engineering from Seoul National University, Seoul, Korea, in 2002, and then acquired the Ph.D. degree from Seoul National University in 2008. He is currently an associate professor at the Department of Aerospace Engineering, Chungnam National University, Korea. Previously he was a research fellow and a lecturer at Cranfield University, United Kingdom, in 2008–2012. His research interests cover nonlinear guidance and control, estimation, sensor and information fusion, fault diagnosis, fault tolerant control, and decision making for unmanned systems.

Antonios Tsourdos obtained a MEng on Electronic, Control and Systems Engineering from the University of Sheffield (1995), an MSc on Systems Engineering from Cardiff University (1996) and a PhD on Nonlinear Robust Missile Autopilot Design and Analysis from Cranfield University (1999). He is a Professor of Control Engineering with Cranfield University. Appointed Head of the Cyber-Physical Systems in 2013. Professor Tsourdos was member of the Team Stellar, the winning team for the UK MoD Grand Challenge (2008) and the IET Innovation Award (Category Team, 2009).

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Shin, J., Kim, S. & Tsourdos, A. Neural-networks-based Adaptive Control for an Uncertain Nonlinear System with Asymptotic Stability. Int. J. Control Autom. Syst. 16, 1989–2001 (2018). https://doi.org/10.1007/s12555-017-0641-x

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  • DOI: https://doi.org/10.1007/s12555-017-0641-x

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