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Adaptive neural network-based saturated control of robotic exoskeletons

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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.

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Authors and Affiliations

  1. Control System Laboratory, Department of Advanced Science and Technology, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, 468-8511, Nagoya, Japan

    Hamed Jabbari Asl, Tatsuo Narikiyo & Michihiro Kawanishi

  2. Faculty of Engineering, Department of Mechatronics Engineering, Izmir University of Economics, Balova, 35330, Izmir, Turkey

    Hamed Jabbari Asl

Authors
  1. Hamed Jabbari Asl
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  2. Tatsuo Narikiyo
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  3. Michihiro Kawanishi
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Correspondence to Hamed Jabbari Asl.

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

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Jabbari Asl, H., Narikiyo, T. & Kawanishi, M. Adaptive neural network-based saturated control of robotic exoskeletons. Nonlinear Dyn 94, 123–139 (2018). https://doi.org/10.1007/s11071-018-4348-1

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