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Design Novel Nonlinear Controller Applied to Robot Manipulator: Design New Feedback Linearization Fuzzy Controller With Minimum Rule Base Tuning Method
Farzin Piltan , Mohammad Keshavarz, Ali Badri, Arash Zargari
Pages - 1 - 12 | Revised - 15-01-2012 | Published - 21-02-2012
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KEYWORDS
Feedback Linearization Controller, Fuzzy Logic Methodology, Feedback linearization Fuzzy Controller, Adaptive Methodology, Fuzzy Adaptive Feedback Linearization Fuzzy Method
ABSTRACT
In this paper, fuzzy adaptive base tuning feedback linearization fuzzy methodology to adaption gain is introduced. The system performance in feedback linearization controller and feedback linearization fuzzy controller are sensitive to the main controller coefficient. Therefore, compute the optimum value of main controller coefficient for a system is the main important challenge work. This problem has solved by adjusting main fuzzy controller continuously in real-time. In this way, the overall system performance has improved with respect to the classical feedback linearization controller and feedback linearization fuzzy controller. Adaptive feedback linearization fuzzy controller solved external disturbance as well as mathematical nonlinear equivalent part by applied fuzzy supervisory method in feedback linearization fuzzy controller. The addition of an adaptive law to a feedback linearization fuzzy controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. Refer to this research; tuning methodology can online adjust coefficient parts of the fuzzy rules. Since this algorithm for is specifically applied to a robot manipulator.
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8 | Khalilian, A., Piltan, F., Avatefipour, O., Nasrabad, M. R. S., & Sahamijoo, G. (2014). Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator. International Journal of Intelligent Systems and Applications (IJISA), 6(9), 75. |
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14 | Piltan, F., Rahbar, S., Pooladi, E., & Davarpanah, H. (2014). Design New PID like Fuzzy CTC Controller: Applied to Spherical Motor. International Journal of Modern Education and Computer Science (IJMECS), 6(5), 60. |
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51 | Piltan, F., Bayat, R., Mehara, S., & Meigolinedjad, J. (2012). GDO Artificial Intelligence-Based Switching PID Baseline Feedback Linearization Method: Controlled PUMA Workspace. International Journal of Information Engineering and Electronic Business (IJIEEB), 4(5), 17. |
52 | Piltan, F., Bayat, R., Aghayari, F., & Boroomand, B. (2012). Design Error-Based Linear Model-Free Evaluation Performance Computed Torque Controller. International Journal of Robotics and Automation, 3(3), 151-166. |
53 | Piltan, F., Jahed, A., Rezaie, H., & Boroomand, B. (2012). Methodology of Robust Linear On-line High Speed Tuning for Stable Sliding Mode Controller: Applied to Nonlinear System. International Journal of Control and Automation, 5(3), 217-236. |
54 | Piltan, F., Meigolinedjad, J., Mehrara, S., & Rahmdel, S. (2012). Evaluation Performance of 2nd Order Nonlinear System: Baseline Control Tunable Gain Sliding Mode Methodology. International Journal of Robotics and Automation, 3(3), 192-211. |
55 | Piltan, F., Boroomand, B., Jahed, A., & Rezaie, H. (2012). Performance-Based Adaptive Gradient Descent Optimal Coefficient Fuzzy Sliding Mode Methodology. International Journal of Intelligent Systems and Applications (IJISA), 4(11), 40. |
56 | Piltan, F., Mirzaei, M., Shahriari, F., Nazari, I., & Emamzadeh, S. (2012). Design Baseline Computed Torque Controller. International Journal of Engineering, 6(3), 129-141. |
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Dr. Farzin Piltan
SSP Co - Iran
SSP.ROBOTIC@yahoo.com
Mr. Mohammad Keshavarz
- Iran
Mr. Ali Badri
- Iran
Mr. Arash Zargari
- Iran
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