Practical implementation of a type-2 fuzzy logic controller for steering a service robot
Service robots are designed to assist humans in various tasks and often rely on wheeled locomotion for navigation. Effective robot movement requires a robust control system to regulate steering and ensure precise maneuvering toward locations. However, a common challenge in service robot navigation i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266672072500044X |
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| author | Suci Dwijayanti Bhakti Y. Suprapto Ichlasul A. Rizky |
| author_facet | Suci Dwijayanti Bhakti Y. Suprapto Ichlasul A. Rizky |
| author_sort | Suci Dwijayanti |
| collection | DOAJ |
| description | Service robots are designed to assist humans in various tasks and often rely on wheeled locomotion for navigation. Effective robot movement requires a robust control system to regulate steering and ensure precise maneuvering toward locations. However, a common challenge in service robot navigation is the lack of precision in steering control. To address this issue, this study implements and evaluates a steering control system for wheeled service robots using a type-2 fuzzy logic controller (T2-FLC). The proposed T2-FLC system incorporates two input variables: error (difference between the setpoint determined by the light detection and ranging sensor and the steering encoder reading) and de-error (difference between the current and previous error values). Subsequently, these inputs are converted into three, five, or seven membership functions (MFs). Comparative simulation analysis revealed that the T2-FLC with seven MFs outperformed that with alternative MF configurations and a conventional type-1 FLC and achieved a minimal steady-state error of 0.0118. Real-time experiments further validated these findings, with the seven-MF T2-FLC producing a steady-state error of only 3.6 during a 90° setpoint test. In obstacle navigation trials, a T2-FLC-equipped robot navigated to target destinations in 32.49 s in stationary obstacle scenarios and within 41.78 s in dynamic obstacle environments. These findings confirm that the T2-FLC significantly enhances steering performance, making it viable for controlling service robot navigation. |
| format | Article |
| id | doaj-art-e3ae98abde174f0ebeb48d0368fb27d1 |
| institution | OA Journals |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-e3ae98abde174f0ebeb48d0368fb27d12025-08-20T02:35:47ZengElsevierResults in Control and Optimization2666-72072025-06-011910055810.1016/j.rico.2025.100558Practical implementation of a type-2 fuzzy logic controller for steering a service robotSuci Dwijayanti0Bhakti Y. Suprapto1Ichlasul A. Rizky2Corresponding author.; Department of Electrical Engineering, Jl. Palembang Prabumulih KM 32 Ogan Ilir, Indralaya 30662, IndonesiaDepartment of Electrical Engineering, Jl. Palembang Prabumulih KM 32 Ogan Ilir, Indralaya 30662, IndonesiaDepartment of Electrical Engineering, Jl. Palembang Prabumulih KM 32 Ogan Ilir, Indralaya 30662, IndonesiaService robots are designed to assist humans in various tasks and often rely on wheeled locomotion for navigation. Effective robot movement requires a robust control system to regulate steering and ensure precise maneuvering toward locations. However, a common challenge in service robot navigation is the lack of precision in steering control. To address this issue, this study implements and evaluates a steering control system for wheeled service robots using a type-2 fuzzy logic controller (T2-FLC). The proposed T2-FLC system incorporates two input variables: error (difference between the setpoint determined by the light detection and ranging sensor and the steering encoder reading) and de-error (difference between the current and previous error values). Subsequently, these inputs are converted into three, five, or seven membership functions (MFs). Comparative simulation analysis revealed that the T2-FLC with seven MFs outperformed that with alternative MF configurations and a conventional type-1 FLC and achieved a minimal steady-state error of 0.0118. Real-time experiments further validated these findings, with the seven-MF T2-FLC producing a steady-state error of only 3.6 during a 90° setpoint test. In obstacle navigation trials, a T2-FLC-equipped robot navigated to target destinations in 32.49 s in stationary obstacle scenarios and within 41.78 s in dynamic obstacle environments. These findings confirm that the T2-FLC significantly enhances steering performance, making it viable for controlling service robot navigation.http://www.sciencedirect.com/science/article/pii/S266672072500044XService robotSteering controlType-1 fuzzy logic controllerType-2 fuzzy logic controllerObstacles |
| spellingShingle | Suci Dwijayanti Bhakti Y. Suprapto Ichlasul A. Rizky Practical implementation of a type-2 fuzzy logic controller for steering a service robot Results in Control and Optimization Service robot Steering control Type-1 fuzzy logic controller Type-2 fuzzy logic controller Obstacles |
| title | Practical implementation of a type-2 fuzzy logic controller for steering a service robot |
| title_full | Practical implementation of a type-2 fuzzy logic controller for steering a service robot |
| title_fullStr | Practical implementation of a type-2 fuzzy logic controller for steering a service robot |
| title_full_unstemmed | Practical implementation of a type-2 fuzzy logic controller for steering a service robot |
| title_short | Practical implementation of a type-2 fuzzy logic controller for steering a service robot |
| title_sort | practical implementation of a type 2 fuzzy logic controller for steering a service robot |
| topic | Service robot Steering control Type-1 fuzzy logic controller Type-2 fuzzy logic controller Obstacles |
| url | http://www.sciencedirect.com/science/article/pii/S266672072500044X |
| work_keys_str_mv | AT sucidwijayanti practicalimplementationofatype2fuzzylogiccontrollerforsteeringaservicerobot AT bhaktiysuprapto practicalimplementationofatype2fuzzylogiccontrollerforsteeringaservicerobot AT ichlasularizky practicalimplementationofatype2fuzzylogiccontrollerforsteeringaservicerobot |