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2019 | 121 | 14-21
Article title

Neural Network based control of Robot Manipulator

Content
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EN
Abstracts
EN
This article proposes an RBFNN (Radial Basis Function Neural Network) and sliding mode based controller to manipulate the robot manipulator. The technique used has been based on a sliding mode control approach that can drive the system towards a sliding surface by Gaussian radial basis function neural network based tuned-controller.
Year
Volume
121
Pages
14-21
Physical description
Contributors
author
  • General Electric, Bangalore, India
author
  • KIET Group of Institutions, Ghaziabad, India
References
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  • [12] Topalov AV, Kaynak O. Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism. Journal of Process Control 2007; 14: 581–589
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  • [15] Sun, T. Y.; Hsieh, S. T. & Lin, C. W. (2005). Particle Swarm Optimization Incorporated with Disturbance for Improving the Efficiency of Macrocell Overlap Removal and Placement. Proceeding of The 2005 International Conference on Artificial Intelligence, pp. 122-125
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  • [17] Efe MO, Kaynak O, Yu X. Sliding mode control of three degrees of freedom anthropoid robot by driving the controller parameters to an equivalent regime. Journal of Dynamic Systems, Measurement and Control (ASME) 2010; 122: 632–640
  • [18] Topalov AV, Kaynak O. On-line learning in adaptive neurocontrol schemes with a sliding mode algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part B 2011; 31(3): 445–450
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-fcb7c437-2d3b-4fcb-af21-64865eebf2c5
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