Adaptive neural network-based saturated control of robotic exoskeletons

  • Hamed Jabbari Asl
  • Tatsuo Narikiyo
  • Michihiro Kawanishi
Original Paper
  • 8 Downloads

Abstract

In this paper, novel adaptive neural network (NN) controllers with input saturation are presented for n-link robotic exoskeletons. The controllers consist of a state feedback controller and an output feedback controller. Through utilizing auxiliary dynamics, the controllers provide a new framework for input saturated control of these robotic systems which can feature the global stability for state feedback control. To compensate for the unknown dynamics of the system, adaptive schemes based on NNs are exploited. Furthermore, adaptive robust terms are utilized to deal with unknown external disturbances. Stability studies show that the closed-loop system is globally uniformly ultimately bounded (UUB) with the state feedback controller, where the global property of the NN-based controller is achieved exploiting a smooth switching function and a robust control term. Also, the system is semi-globally UUB with the output feedback controller. Effectiveness of the controllers is validated by simulations and experimental tests.

Keywords

Robotic exoskeleton Neural network Adaptive control Bounded-input control 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest to disclose.

Ethics committee approval

This work has been approved by the Screening Committee of Toyota Technological Institute.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Hamed Jabbari Asl
    • 1
    • 2
  • Tatsuo Narikiyo
    • 1
  • Michihiro Kawanishi
    • 1
  1. 1.Control System Laboratory, Department of Advanced Science and TechnologyToyota Technological InstituteTempaku-kuJapan
  2. 2.Faculty of Engineering, Department of Mechatronics EngineeringIzmir University of EconomicsBalovaTurkey

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