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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500009X |
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| Summary: | 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. |
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| ISSN: | 2590-1230 |