A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems.
This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PI...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
SAGE Publications
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/12702/ http://psasir.upm.edu.my/id/eprint/12702/1/A%20genetically%20trained%20adaptive%20neuro.pdf |
| _version_ | 1848841906949193728 |
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| author | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Ali Abbas, Kassim |
| author_facet | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Ali Abbas, Kassim |
| author_sort | Lutfy, Omar Farouq |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters.
To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller. |
| first_indexed | 2025-11-15T07:50:41Z |
| format | Article |
| id | upm-12702 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T07:50:41Z |
| publishDate | 2009 |
| publisher | SAGE Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-127022015-10-05T06:06:42Z http://psasir.upm.edu.my/id/eprint/12702/ A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Ali Abbas, Kassim This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller. SAGE Publications 2009-05-01 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/12702/1/A%20genetically%20trained%20adaptive%20neuro.pdf Lutfy, Omar Farouq and Mohd Noor, Samsul Bahari and Marhaban, Mohammad Hamiruce and Ali Abbas, Kassim (2009) A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 223 (3). pp. 309-321. ISSN 0959-6518 10.1243/09596518JSCE683 English |
| spellingShingle | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Ali Abbas, Kassim A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title | A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title_full | A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title_fullStr | A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title_full_unstemmed | A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title_short | A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| title_sort | genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. |
| url | http://psasir.upm.edu.my/id/eprint/12702/ http://psasir.upm.edu.my/id/eprint/12702/ http://psasir.upm.edu.my/id/eprint/12702/1/A%20genetically%20trained%20adaptive%20neuro.pdf |