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Neural Network Control of a Rehabilitation by State and Output Feedback.pdf (931.32 kB)

Neural network control of a rehabilitation robot by state and output feedback

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posted on 2023-06-09, 09:23 authored by Wei He, Shuzhi Sam Ge, Yanan LiYanan Li, Effie Chew, Yee Sien Ng
In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Journal of Intelligent & Robotic Systems

ISSN

0921-0296

Publisher

Springer Verlag

Issue

1

Volume

80

Page range

15-31

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-12-15

First Open Access (FOA) Date

2017-12-15

First Compliant Deposit (FCD) Date

2017-12-14

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