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Neural Computing and Applications

Erschienen in:

17.11.2016 | Original Article

Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks

verfasst von: Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta

Erschienen in: Neural Computing and Applications | Ausgabe 1/2018

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Abstract

Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.

Vorheriger Artikel Robust stability analysis for uncertain recurrent neural networks with leakage delay based on delay-partitioning approach

Nächster Artikel Multi-criteria group decision-making method based on interdependent inputs of single-valued trapezoidal neutrosophic information

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Titel: Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks
verfasst von: Rajesh Kumar
Smriti Srivastava
J. R. P. Gupta
Publikationsdatum: 17.11.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2695-8

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