research-article

Backstepping terminal sliding mode control of robot manipulator using radial basis functional neural networks

Authors:

Debashisha JenaAuthors Info & Claims

Volume 67, Issue C

Pages 690 - 707

https://doi.org/10.1016/j.compeleceng.2017.11.007

Published: 01 April 2018 Publication History

Abstract

This paper examines an observer-based backstepping terminal sliding mode controller (BTSMC) for 3 degrees of freedom overhead transmission line de-icing robot manipulator (OTDIRM). The control law for tracking of the OTDIRM is formulated by the combination of BTSMC and neural network (NN) based approximation. For the precise trajectory tracking performance and enhanced disturbance rejection, NN-based adaptive observer backstepping terminal sliding mode control (NNAOBTSMC) is developed. To obviate local minima problem, the weights of both NN observer and NN approximator are adjusted off-line using particle swarm optimization. The radial basis function neural network-based observer is used to estimate tracking position and velocity vectors of the OTDIRM. The stability of the proposed control methods is verified with the Lyapunov stability theorem. Finally, the robustness of the proposed NNAOBTSMC is checked against input disturbances and uncertainties.

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Cited By

Zhang Xin Quan Ying (2023)Fuzzy Sliding Mode Control of Manipulator Based on Disturbance Observer and RBF Neural NetworkAutomatic Control and Computer Sciences10.3103/S014641162302009857:2(123-134)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.3103/S0146411623020098
Liu SLian BDan Z(2023)Neural adaptive robust control for MEMS gyroscope with output constraintsTelecommunications Systems10.1007/s11235-023-01047-984:2(203-213)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11235-023-01047-9
Chaudhary ARaheja JWang G(2018)Introduction to the Special Section on Learning-based Decision Making in RoboticsComputers and Electrical Engineering10.1016/j.compeleceng.2018.04.01167:C(822-824)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1016/j.compeleceng.2018.04.011

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Backstepping terminal sliding mode control of robot manipulator using radial basis functional neural networks

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Published In

cover image Computers and Electrical Engineering

Computers and Electrical Engineering Volume 67, Issue C

Apr 2018

851 pages

ISSN:0045-7906

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2018

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Cited By

Zhang Xin Quan Ying (2023)Fuzzy Sliding Mode Control of Manipulator Based on Disturbance Observer and RBF Neural NetworkAutomatic Control and Computer Sciences10.3103/S014641162302009857:2(123-134)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.3103/S0146411623020098
Liu SLian BDan Z(2023)Neural adaptive robust control for MEMS gyroscope with output constraintsTelecommunications Systems10.1007/s11235-023-01047-984:2(203-213)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11235-023-01047-9
Chaudhary ARaheja JWang G(2018)Introduction to the Special Section on Learning-based Decision Making in RoboticsComputers and Electrical Engineering10.1016/j.compeleceng.2018.04.01167:C(822-824)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1016/j.compeleceng.2018.04.011

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