Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system
Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-03-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/14613484241287608 |
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| author | NIzaruddin M Nasir Nor Maniha Abdul Ghani Ahmad Nor Kasruddin Nasir Mohd Ashraf Ahmad Mohammad Osman Tokhi |
| author_facet | NIzaruddin M Nasir Nor Maniha Abdul Ghani Ahmad Nor Kasruddin Nasir Mohd Ashraf Ahmad Mohammad Osman Tokhi |
| author_sort | NIzaruddin M Nasir |
| collection | DOAJ |
| description | Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort. |
| format | Article |
| id | doaj-art-c36bf74feb644b62b01440e3bff2bbba |
| institution | DOAJ |
| issn | 1461-3484 2048-4046 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Low Frequency Noise, Vibration and Active Control |
| spelling | doaj-art-c36bf74feb644b62b01440e3bff2bbba2025-08-20T02:57:55ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462025-03-014410.1177/14613484241287608Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair systemNIzaruddin M NasirNor Maniha Abdul GhaniAhmad Nor Kasruddin NasirMohd Ashraf AhmadMohammad Osman TokhiTwo-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.https://doi.org/10.1177/14613484241287608 |
| spellingShingle | NIzaruddin M Nasir Nor Maniha Abdul Ghani Ahmad Nor Kasruddin Nasir Mohd Ashraf Ahmad Mohammad Osman Tokhi Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system Journal of Low Frequency Noise, Vibration and Active Control |
| title | Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system |
| title_full | Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system |
| title_fullStr | Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system |
| title_full_unstemmed | Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system |
| title_short | Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system |
| title_sort | neuro modelling and fuzzy logic control of a two wheeled wheelchair system |
| url | https://doi.org/10.1177/14613484241287608 |
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