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| Design of Model Free Adaptive Fuzzy Computed Torque Controller for a Nonlinear Second Order System
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Full
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Source |
International Journal of Robotics and Automation (IJRA) |
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Table of Contents |
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Volume: 2 Issue: 4 |
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Pages: NULL |
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Publication
Date: September / October 2011 |
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ISSN
(Online): 2180-1312 |
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Pages |
232 - 244 |
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Author(s) |
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Published
Date |
05-10-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Adaptive Fuzzy Computed Torque Controller, Robot Manipulator, Classical Control, Non-classical Control, Computed Torque Controller |
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| In this study, a model free adaptive fuzzy computed torque controller (AFCTC) is designed for a two-degree-of freedom robot manipulator to rich the best performance. Computed torque controller is studied because of its high performance. AFCTC has been also included in this study because of its robust character and high performance. Besides, this control method can be applied to non-linear systems easily. Today, robot manipulators are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools are used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). The strategies of control robot manipulator are classified into two main groups: classical and non-classical methods, however both classical and non-classical theories have been applied successfully in many applications, but they also have some limitation. One of the most important nonlinear robust controller that can used in uncertainty nonlinear systems, are computed torque controller. This paper is focuses on applied non-classical method in robust classical method to reduce the limitations. Therefore adaptive fuzzy computed torque controller will be presented in this paper. |
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Piltan, F., et al., “Design Mathematical Tunable Gain PID-Like Sliding Mode Fuzzy Controller with Minimum Rule Base,” International Journal of Robotic and Automation, 2 (3): 146-156, 2011.
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| Farzin Piltan : Colleagues
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| N. Sulaiman : Colleagues
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| Amin Jalali : Colleagues
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| Fereshteh Danesh Narouei : Colleagues
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