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Neural Computing and Applications
Published in: Neural Computing and Applications 11/2021

06-10-2020 | Original Article

Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators

Authors: Jinzhu Peng, Shuai Ding, Rickey Dubay

Published in: Neural Computing and Applications | Issue 11/2021

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Abstract

This paper presents an adaptive backstepping control scheme for electrically driven robotic manipulator (EDRM) system with uncertainties and external disturbances by using neural network disturbance observer (NNDO) and dynamic surface control (DSC) design technique. NNDO is employed to estimate the uncertainties and external disturbances such that the priori information of the unknown dynamics will not be needed. To overcome the problem of “explosion of complexity” inherent in the backstepping design method, the DSC technique is integrated into the adaptive backstepping control design framework, where the NNDOs with adaptive composite law are utilized to compensate the uncertainties and external disturbances of EDRM. Based on the Lyapunov stability theory, it can be proven that the closed-loop system is stable in the sense that all the variables are guaranteed to be uniformly ultimately bounded. The results of simulation and experimental tests demonstrate the approximation capability of NNDO and the effectiveness of the proposed adaptive DSC scheme.
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Metadata
Title
Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators
Authors
Jinzhu Peng
Shuai Ding
Rickey Dubay
Publication date
06-10-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05391-8

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