Intelligent control for visual servoing system
This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color thres...
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science
2017
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/20433/ http://doi.org/10.11591/ijeecs.v6.i1.pp72-79 |
| _version_ | 1848827267250126848 |
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| author | Pebrianti, Dwi Ong , Ying Peh Rosdiyana, Samad Mahfuzah, Mustafa Bayuaji, Luhur Nor Rul Hasma, Abdullah |
| author_facet | Pebrianti, Dwi Ong , Ying Peh Rosdiyana, Samad Mahfuzah, Mustafa Bayuaji, Luhur Nor Rul Hasma, Abdullah |
| author_sort | Pebrianti, Dwi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color threshold method is used for object tracking and recognition. Two different control methods, PID and Fuzzy Logic Control (FLC) are designed and the performances are compared through simulation. From the simulation result, the settling time of PID controller is 40 times faster than FLC. Additionally, the rise time of PID is about 20 times faster than FLC. However, the overshoot percentage of PID controller is 4 times higher than FLC. High overshoot value is not preferable in a control system, since it will cause the damage to the system. Real implementation of FLC on a home-built visual servoing system is conducted. Two different types of FLC, 9 and 11 rules of FLC are designed and implemented on the system. The experimental result shows that FLC with different total number of rules give different system performance. The settling time of FLC with 11 rules is 2 times faster than FLC with 9 rules. Additionally, the overshoot percentage of FLC with 11 rules is 2 times lower than FLC with 9 rules. |
| first_indexed | 2025-11-15T02:19:58Z |
| format | Article |
| id | ump-20433 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:00Z |
| publishDate | 2017 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-204332025-09-08T00:53:52Z https://umpir.ump.edu.my/id/eprint/20433/ Intelligent control for visual servoing system Pebrianti, Dwi Ong , Ying Peh Rosdiyana, Samad Mahfuzah, Mustafa Bayuaji, Luhur Nor Rul Hasma, Abdullah QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color threshold method is used for object tracking and recognition. Two different control methods, PID and Fuzzy Logic Control (FLC) are designed and the performances are compared through simulation. From the simulation result, the settling time of PID controller is 40 times faster than FLC. Additionally, the rise time of PID is about 20 times faster than FLC. However, the overshoot percentage of PID controller is 4 times higher than FLC. High overshoot value is not preferable in a control system, since it will cause the damage to the system. Real implementation of FLC on a home-built visual servoing system is conducted. Two different types of FLC, 9 and 11 rules of FLC are designed and implemented on the system. The experimental result shows that FLC with different total number of rules give different system performance. The settling time of FLC with 11 rules is 2 times faster than FLC with 9 rules. Additionally, the overshoot percentage of FLC with 11 rules is 2 times lower than FLC with 9 rules. Institute of Advanced Engineering and Science 2017 Article PeerReviewed pdf en cc_by_nc_sa_4 https://umpir.ump.edu.my/id/eprint/20433/1/Intelligent%20Control%20for%20Visual%20Servoing%20System.pdf pdf en https://umpir.ump.edu.my/id/eprint/20433/2/Intelligent%20Control%20for%20Visual%20Servoing%20System%201.pdf Pebrianti, Dwi and Ong , Ying Peh and Rosdiyana, Samad and Mahfuzah, Mustafa and Bayuaji, Luhur and Nor Rul Hasma, Abdullah (2017) Intelligent control for visual servoing system. Indonesian Journal of Electrical Engineering and Computer Science, 6 (1). pp. 72-79. ISSN 2502-4752. (Published) http://doi.org/10.11591/ijeecs.v6.i1.pp72-79 http://doi.org/10.11591/ijeecs.v6.i1.pp72-79 http://doi.org/10.11591/ijeecs.v6.i1.pp72-79 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Pebrianti, Dwi Ong , Ying Peh Rosdiyana, Samad Mahfuzah, Mustafa Bayuaji, Luhur Nor Rul Hasma, Abdullah Intelligent control for visual servoing system |
| title | Intelligent control for visual servoing system |
| title_full | Intelligent control for visual servoing system |
| title_fullStr | Intelligent control for visual servoing system |
| title_full_unstemmed | Intelligent control for visual servoing system |
| title_short | Intelligent control for visual servoing system |
| title_sort | intelligent control for visual servoing system |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/20433/ https://umpir.ump.edu.my/id/eprint/20433/ https://umpir.ump.edu.my/id/eprint/20433/ http://doi.org/10.11591/ijeecs.v6.i1.pp72-79 |