A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems
This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the propose...
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| Format: | Article |
| Language: | English |
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Academic Journals
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/22782/ http://psasir.upm.edu.my/id/eprint/22782/1/22782.pdf |
| _version_ | 1848844579402416128 |
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| author | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce |
| author_facet | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce |
| author_sort | Lutfy, Omar F. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the proposed controller possesses several advantages over the conventional ANFIS controller especially the reduction in execution time, and hence, it is more appropriate for real time control. A real-coded genetic algorithm (GA) was utilized to optimize the premise and the consequent parameters of the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Accordingly, the necessity for the teaching signal required by other optimization techniques has been eliminated. Furthermore, the GA was employed to determine the input and output scaling factors for this controller, instead of the widely used trial and error method. Two nonlinear MIMO systems were chosen to be controlled by this controller. In addition, the controller robustness to output disturbances was also investigated and the results clearly showed the notable accuracy and the generalization ability of this controller. Moreover, the result of a comparative study with a conventional MIMO ANFIS controller has indicated the superiority of the simplified MIMO ANFIS controller. |
| first_indexed | 2025-11-15T08:33:10Z |
| format | Article |
| id | upm-22782 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:33:10Z |
| publishDate | 2011 |
| publisher | Academic Journals |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-227822020-04-15T16:22:31Z http://psasir.upm.edu.my/id/eprint/22782/ A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the proposed controller possesses several advantages over the conventional ANFIS controller especially the reduction in execution time, and hence, it is more appropriate for real time control. A real-coded genetic algorithm (GA) was utilized to optimize the premise and the consequent parameters of the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Accordingly, the necessity for the teaching signal required by other optimization techniques has been eliminated. Furthermore, the GA was employed to determine the input and output scaling factors for this controller, instead of the widely used trial and error method. Two nonlinear MIMO systems were chosen to be controlled by this controller. In addition, the controller robustness to output disturbances was also investigated and the results clearly showed the notable accuracy and the generalization ability of this controller. Moreover, the result of a comparative study with a conventional MIMO ANFIS controller has indicated the superiority of the simplified MIMO ANFIS controller. Academic Journals 2011 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/22782/1/22782.pdf Lutfy, Omar F. and Mohd Noor, Samsul Bahari and Marhaban, Mohammad Hamiruce (2011) A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems. Scientific Research and Essays, 6 (31). art. no. 5196FB832036. pp. 6475-6486. ISSN 1992-2248 https://academicjournals.org/journal/SRE/article-abstract/5196FB832036 10.5897/SRE11.1059 |
| spellingShingle | Lutfy, Omar F. Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title | A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title_full | A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title_fullStr | A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title_full_unstemmed | A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title_short | A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| title_sort | simplified adaptive neuro-fuzzy inference system (anfis) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems |
| url | http://psasir.upm.edu.my/id/eprint/22782/ http://psasir.upm.edu.my/id/eprint/22782/ http://psasir.upm.edu.my/id/eprint/22782/ http://psasir.upm.edu.my/id/eprint/22782/1/22782.pdf |