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Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties

Published online by Cambridge University Press:  14 November 2024

Qi Chen Open the ORCID record for Qi Chen [Opens in a new window] ,
Chengjun Ming  and
Yanan Qin
Qi Chen*
Affiliation:
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
Chengjun Ming
Affiliation:
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
Yanan Qin
Affiliation:
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
*
Corresponding author: Qi Chen; Email: [email protected]
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Abstract

In this paper, a novel fast nonsingular integral terminal sliding mode controller based on an adaptive neural network (ANN-FNITSMC) is proposed for the trajectory tracking control of cable-driven continuum robots (CDCRs) in complex underwater environments with uncertainties. First, a novel fast nonsingular integral terminal sliding mode control (FNITSMC) is designed to solve the chattering and singularity problems of the conventional terminal sliding mode control (TSMC). Second, an adaptive neural network (ANN) based on a radial basis function (RBF) is established to derive the uncertainties and compensate for the control input of CDCRs, enabling high-stable accuracy and strong robustness trajectory tracking in complex underwater environments. Simulation results are presented to demonstrate the high accuracy and strong robustness of the ANN-FNITSMC. Finally, the high accuracy, high stability, and strong robustness of the proposed trajectory tracking strategy are verified through an underwater experiment platform.

Keywords

cable-driven continuum robotssliding mode controladaptive neural networktrajectory tracking control
Type
Research Article
Information
Robotica , Volume 43 , Issue 1 , January 2025 , pp. 316 - 331
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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