Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network
This study addresses the challenge of optimizing the performance of anti-lock braking systems (ABS) to enhance vehicle safety and improve operational efficiency. The research introduces a novel control strategy that combines fixed-time sliding mode control (SMC), artificial neural networks (ANN), Ta...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500009X |
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| author | Najlae Jennan El Mehdi Mellouli |
| author_facet | Najlae Jennan El Mehdi Mellouli |
| author_sort | Najlae Jennan |
| collection | DOAJ |
| description | This study addresses the challenge of optimizing the performance of anti-lock braking systems (ABS) to enhance vehicle safety and improve operational efficiency. The research introduces a novel control strategy that combines fixed-time sliding mode control (SMC), artificial neural networks (ANN), Takagi-Sugeno (T-S) fuzzy logic, and particle swarm optimization (PSO). The ABS system is modelled and controlled using a fixed-time SMC approach, with T-S fuzzy logic employed to approximate the friction function of the ABS model. ANN is used to approximate the reaching law, ensuring optimal fixed-time convergence. PSO is then employed to optimize an additive term in the reaching law, with the aim of reducing errors from the ANN approximation. The stability of the overall system has been validated using the Lyapunov approach. The results of simulations demonstrate that the proposed method offers a significant improvement in braking performance compared to existing methods. This approach achieves better system stability, reduced chattering and enhanced braking efficiency. |
| format | Article |
| id | doaj-art-7ccf7a07fe8d45ebb302264f0236afdf |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-7ccf7a07fe8d45ebb302264f0236afdf2025-01-11T06:41:53ZengElsevierResults in Engineering2590-12302025-03-0125103921Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural networkNajlae Jennan0El Mehdi Mellouli1Corresponding author.; Laboratory of Engineering, Systems and Applications, Sidi Mohamed Ben Abdellah University, National School of Applied Sciences, Fez, 30050, MoroccoLaboratory of Engineering, Systems and Applications, Sidi Mohamed Ben Abdellah University, National School of Applied Sciences, Fez, 30050, MoroccoThis study addresses the challenge of optimizing the performance of anti-lock braking systems (ABS) to enhance vehicle safety and improve operational efficiency. The research introduces a novel control strategy that combines fixed-time sliding mode control (SMC), artificial neural networks (ANN), Takagi-Sugeno (T-S) fuzzy logic, and particle swarm optimization (PSO). The ABS system is modelled and controlled using a fixed-time SMC approach, with T-S fuzzy logic employed to approximate the friction function of the ABS model. ANN is used to approximate the reaching law, ensuring optimal fixed-time convergence. PSO is then employed to optimize an additive term in the reaching law, with the aim of reducing errors from the ANN approximation. The stability of the overall system has been validated using the Lyapunov approach. The results of simulations demonstrate that the proposed method offers a significant improvement in braking performance compared to existing methods. This approach achieves better system stability, reduced chattering and enhanced braking efficiency.http://www.sciencedirect.com/science/article/pii/S259012302500009XABSANNFixed-timeFuzzy logicPSOSMC |
| spellingShingle | Najlae Jennan El Mehdi Mellouli Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network Results in Engineering ABS ANN Fixed-time Fuzzy logic PSO SMC |
| title | Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network |
| title_full | Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network |
| title_fullStr | Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network |
| title_full_unstemmed | Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network |
| title_short | Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network |
| title_sort | optimal fixed time sliding mode control for anti lock braking systems based fuzzy logic and neural network |
| topic | ABS ANN Fixed-time Fuzzy logic PSO SMC |
| url | http://www.sciencedirect.com/science/article/pii/S259012302500009X |
| work_keys_str_mv | AT najlaejennan optimalfixedtimeslidingmodecontrolforantilockbrakingsystemsbasedfuzzylogicandneuralnetwork AT elmehdimellouli optimalfixedtimeslidingmodecontrolforantilockbrakingsystemsbasedfuzzylogicandneuralnetwork |