Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part...

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Bibliographic Details
Published in:Journal of vibration and control 2022-08, Vol.28 (15-16), p.1992-2003
Main Authors: Obadina, Ololade O, Thaha, Mohamed A, Althoefer, Kaspar, Shaheed, Mohammad H
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive moving-average models
Manipulators
Parameter identification
Particle swarm optimization
Robot arms
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Summary:This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.
ISSN:1077-5463
1741-2986
DOI:10.1177/10775463211003402