Design artificial robust control of second order system based on adaptive fuzzy gain scheduling
Refer to the research, a position adaptive fuzzy gain scheduling computed torque controller (AFGSCTC) design and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in computed torqu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
IDOSI Publications
2011
|
| _version_ | 1825925346298429440 |
|---|---|
| author | Piltan, Farzin Salehi, Alireza Sulaiman, Nasri |
| author_facet | Piltan, Farzin Salehi, Alireza Sulaiman, Nasri |
| author_sort | Piltan, Farzin |
| collection | UPM |
| description | Refer to the research, a position adaptive fuzzy gain scheduling computed torque controller (AFGSCTC) design and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in computed torque controller (CTC), fuzzy logic controller and adaptive method, the output has improved. Each method by adding to the previous controller has covered negative points. The main target in this research is analyses and design of the position controller for robot manipulator to reach an acceptable performance. Obviously, robot manipulator is nonlinear and a number of parameters are uncertain, this research focuses on design the best performance computed torque controller with regard to the fuzzy logic to select the best controller for the industrial manipulator. Although CTC controller has acceptable performance with known dynamic parameters but by regarding to uncertainty, the computed torque controller's output has fairly fluctuations. To eliminate CTC's fluctuations with regarding to uncertainty fuzzy logic method applied in computed torque controller. This controller works very well in uncertain environment or various dynamic parameters. This paper focuses on the intelligent control of robot manipulator using Adaptive Fuzzy Gain scheduling computed torque controller (AFGSCTC) and various performance indices like the RMS error and Steady state error are used for test the controller performance. |
| first_indexed | 2024-03-06T07:56:44Z |
| format | Article |
| id | upm.eprints-23388 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T07:56:44Z |
| publishDate | 2011 |
| publisher | IDOSI Publications |
| record_format | dspace |
| spelling | upm.eprints-233882015-11-06T13:43:58Z http://psasir.upm.edu.my/id/eprint/23388/ Design artificial robust control of second order system based on adaptive fuzzy gain scheduling Piltan, Farzin Salehi, Alireza Sulaiman, Nasri Refer to the research, a position adaptive fuzzy gain scheduling computed torque controller (AFGSCTC) design and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in computed torque controller (CTC), fuzzy logic controller and adaptive method, the output has improved. Each method by adding to the previous controller has covered negative points. The main target in this research is analyses and design of the position controller for robot manipulator to reach an acceptable performance. Obviously, robot manipulator is nonlinear and a number of parameters are uncertain, this research focuses on design the best performance computed torque controller with regard to the fuzzy logic to select the best controller for the industrial manipulator. Although CTC controller has acceptable performance with known dynamic parameters but by regarding to uncertainty, the computed torque controller's output has fairly fluctuations. To eliminate CTC's fluctuations with regarding to uncertainty fuzzy logic method applied in computed torque controller. This controller works very well in uncertain environment or various dynamic parameters. This paper focuses on the intelligent control of robot manipulator using Adaptive Fuzzy Gain scheduling computed torque controller (AFGSCTC) and various performance indices like the RMS error and Steady state error are used for test the controller performance. IDOSI Publications 2011 Article PeerReviewed Piltan, Farzin and Salehi, Alireza and Sulaiman, Nasri (2011) Design artificial robust control of second order system based on adaptive fuzzy gain scheduling. World Applied Sciences Journal, 13 (5). pp. 1085-1092. ISSN 1818-4952; ESSN: 1991-6426 http://www.idosi.org/wasj/wasj13%285%292011.htm |
| spellingShingle | Piltan, Farzin Salehi, Alireza Sulaiman, Nasri Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title | Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title_full | Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title_fullStr | Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title_full_unstemmed | Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title_short | Design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| title_sort | design artificial robust control of second order system based on adaptive fuzzy gain scheduling |
| work_keys_str_mv | AT piltanfarzin designartificialrobustcontrolofsecondordersystembasedonadaptivefuzzygainscheduling AT salehialireza designartificialrobustcontrolofsecondordersystembasedonadaptivefuzzygainscheduling AT sulaimannasri designartificialrobustcontrolofsecondordersystembasedonadaptivefuzzygainscheduling |