Intelligent control of twin-rotor MIMO system using fuzzy inference techniques
The flight control system of helicopter is difficult to control for their performance of high-order unstable, nonlinear, and multiple-inputs multiple-outputs (MIMO) system. The behavior of two degree-of-freedom (2-DOF) helicopter has significant cross-coupling between pitch and yaw directional motio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Praise Worthy Prize
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28559/ http://psasir.upm.edu.my/id/eprint/28559/1/Intelligent%20Control%20of%20Twin.pdf |
| _version_ | 1848846150375833600 |
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| author | Che Soh, Azura Abdul Rahman, Ribhan Zafira Md Sarkan, Haslina Yeo, Lay Ting |
| author_facet | Che Soh, Azura Abdul Rahman, Ribhan Zafira Md Sarkan, Haslina Yeo, Lay Ting |
| author_sort | Che Soh, Azura |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The flight control system of helicopter is difficult to control for their performance of high-order unstable, nonlinear, and multiple-inputs multiple-outputs (MIMO) system. The behavior of two degree-of-freedom (2-DOF) helicopter has significant cross-coupling between pitch and yaw directional motions. This study presents a control system for a 2-DOF helicopter plant which more specific presented as twin-rotor MIMO system by using fuzzy logic technique. Fuzzy logic controller (FLC) is designed by using MATLAB to improve the performance of flight control system. Thus, the designed pitch and yaw controller are tuned by adjusting the range of membership function of input variable and the number of fuzzy rules used. The designed FLC are implemented and the results of the simulation are compared with LQR controller. The proposed pitch fuzzy controller shows improvement in rise time from 0.607sec to 0.458s, settling time from 7.188s to 1.866s, and percent overshoot from 38.1% to 0% over the existing pitch LQR controller. Meanwhile, the proposed yaw fuzzy controller shows improvement in rise time from 1.361s to 0.828s, settling time from 9.120sec to 2.517 s, and percent overshoot from 23.51% to 2.86% over the existing yaw LQR controller. Hence, the simulation results show that FLC has great potential than LQR controller for the flight control system. |
| first_indexed | 2025-11-15T08:58:08Z |
| format | Article |
| id | upm-28559 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:58:08Z |
| publishDate | 2013 |
| publisher | Praise Worthy Prize |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-285592016-01-29T02:31:12Z http://psasir.upm.edu.my/id/eprint/28559/ Intelligent control of twin-rotor MIMO system using fuzzy inference techniques Che Soh, Azura Abdul Rahman, Ribhan Zafira Md Sarkan, Haslina Yeo, Lay Ting The flight control system of helicopter is difficult to control for their performance of high-order unstable, nonlinear, and multiple-inputs multiple-outputs (MIMO) system. The behavior of two degree-of-freedom (2-DOF) helicopter has significant cross-coupling between pitch and yaw directional motions. This study presents a control system for a 2-DOF helicopter plant which more specific presented as twin-rotor MIMO system by using fuzzy logic technique. Fuzzy logic controller (FLC) is designed by using MATLAB to improve the performance of flight control system. Thus, the designed pitch and yaw controller are tuned by adjusting the range of membership function of input variable and the number of fuzzy rules used. The designed FLC are implemented and the results of the simulation are compared with LQR controller. The proposed pitch fuzzy controller shows improvement in rise time from 0.607sec to 0.458s, settling time from 7.188s to 1.866s, and percent overshoot from 38.1% to 0% over the existing pitch LQR controller. Meanwhile, the proposed yaw fuzzy controller shows improvement in rise time from 1.361s to 0.828s, settling time from 9.120sec to 2.517 s, and percent overshoot from 23.51% to 2.86% over the existing yaw LQR controller. Hence, the simulation results show that FLC has great potential than LQR controller for the flight control system. Praise Worthy Prize 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28559/1/Intelligent%20Control%20of%20Twin.pdf Che Soh, Azura and Abdul Rahman, Ribhan Zafira and Md Sarkan, Haslina and Yeo, Lay Ting (2013) Intelligent control of twin-rotor MIMO system using fuzzy inference techniques. International Review of Automatic Control, 6 (1). pp. 35-46. ISSN 1974-6059; ESSN: 1974-6067 http://www.praiseworthyprize.org/jsm/index.php?journal=ireaco&page=article&op=view&path[]=10672 |
| spellingShingle | Che Soh, Azura Abdul Rahman, Ribhan Zafira Md Sarkan, Haslina Yeo, Lay Ting Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title | Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title_full | Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title_fullStr | Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title_full_unstemmed | Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title_short | Intelligent control of twin-rotor MIMO system using fuzzy inference techniques |
| title_sort | intelligent control of twin-rotor mimo system using fuzzy inference techniques |
| url | http://psasir.upm.edu.my/id/eprint/28559/ http://psasir.upm.edu.my/id/eprint/28559/ http://psasir.upm.edu.my/id/eprint/28559/1/Intelligent%20Control%20of%20Twin.pdf |