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

  • Jongho Shin
  • Seungkeun KimEmail author
  • Antonios Tsourdos
Regular Papers Intelligent Control and Applications


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.


Adaptive control asymptotic stability fault tolerant flight control neural network RISE feedback 


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.5th R&D Institute 2nd Directorate at Agency for Defense DevelopmentDaejeonKorea
  2. 2.Department of Aerospace EngineeringChungnam National UniversityDaejeonKorea
  3. 3.School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield, BedfordshireUK

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