Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems
The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publications
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/60428/ http://psasir.upm.edu.my/id/eprint/60428/1/Non-linear%20modelling%20and%20control%20of%20a%20conveyor-belt%20grain%20dryer%20utilizing%20neuro-fuzzy%20systems.pdf |
| _version_ | 1848854192868818944 |
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| author | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali |
| author_facet | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali |
| author_sort | Lutfy, Omar Farouq |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional–integral–derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparative study with a genetically tuned conventional PID controller, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process represented by the developed ANFIS model. |
| first_indexed | 2025-11-15T11:05:58Z |
| format | Article |
| id | upm-60428 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:05:58Z |
| publishDate | 2011 |
| publisher | SAGE Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-604282018-05-21T03:41:46Z http://psasir.upm.edu.my/id/eprint/60428/ Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional–integral–derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparative study with a genetically tuned conventional PID controller, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process represented by the developed ANFIS model. SAGE Publications 2011 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60428/1/Non-linear%20modelling%20and%20control%20of%20a%20conveyor-belt%20grain%20dryer%20utilizing%20neuro-fuzzy%20systems.pdf Lutfy, Omar Farouq and Mohd Noor, Samsul Bahari and Marhaban, Mohammad Hamiruce and Abbas, Kassim Ali (2011) Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225 (5). pp. 611-622. ISSN 0959-6518; ESSN: 2041-3041 http://journals.sagepub.com/doi/abs/10.1177/2041304110394559 10.1177/2041304110394559 |
| spellingShingle | Lutfy, Omar Farouq Mohd Noor, Samsul Bahari Marhaban, Mohammad Hamiruce Abbas, Kassim Ali Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title | Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title_full | Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title_fullStr | Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title_full_unstemmed | Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title_short | Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| title_sort | non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems |
| url | http://psasir.upm.edu.my/id/eprint/60428/ http://psasir.upm.edu.my/id/eprint/60428/ http://psasir.upm.edu.my/id/eprint/60428/ http://psasir.upm.edu.my/id/eprint/60428/1/Non-linear%20modelling%20and%20control%20of%20a%20conveyor-belt%20grain%20dryer%20utilizing%20neuro-fuzzy%20systems.pdf |